Update app.py

Minor error log in search function exec

app.py

@@ -1,473 +1,475 @@
-import gradio as gr
-import spaces
-from sentence_transformers import SentenceTransformer
-from sentence_transformers.util import cos_sim
-from sentence_transformers.quantization import quantize_embeddings
-import pymssql
-import os
-import pandas as pd
-from openai import OpenAI
-from pydantic import BaseModel, Field
-import json
-from sentence_transformers import CrossEncoder
-from torch import nn
-import time
-
-
-SqlServer = os.environ['SQL_SERVER']
-SqlDatabase = os.environ['SQL_DB']
-SqlUser = os.environ['SQL_USER']
-SqlPass = os.environ['SQL_PASS']
-
-
-OpenaiApiKey = os.environ.get("OPENAI_API_KEY")
-OpenaiBaseUrl = os.environ.get("OPENAI_BASE_URL","https://generativelanguage.googleapis.com/v1beta/openai")
-
-
-def sql(query,db=SqlDatabase, login_timeout = 120,onConnectionError = None):
-    
-    start_time = time.time()
-    
-    while True:
-        try:
-            cnxn = pymssql.connect(SqlServer,SqlUser,SqlPass,db, login_timeout = 5)
-            break;
-        except Exception as e:
-            if onConnectionError:
-                onConnectionError(e)
-                
-            if time.time() - start_time > login_timeout:
-                raise TimeoutError("SQL Connection Timeout");
-                
-            time.sleep(1)  # Espera 1 segundo antes de tentar novamente
-    
-    
-    cursor = cnxn.cursor()
-    cursor.execute(query)
-    
-    columns = [column[0] for column in cursor.description]
-    results = [dict(zip(columns, row)) for row in cursor.fetchall()]
-    
-    return results;
-
-
-
-@spaces.GPU
-def embed(text):
-        
-    query_embedding = Embedder.encode(text)
-    return query_embedding.tolist();
-
- 
-@spaces.GPU 
-def rerank(query,documents, **kwargs):
-    return Reranker.rank(query, documents, **kwargs)
-
-ClientOpenai = OpenAI(
-    api_key=OpenaiApiKey 
-    ,base_url=OpenaiBaseUrl 
-)
-
-def llm(messages, ResponseFormat = None, **kwargs):
-    
-    fn = ClientOpenai.chat.completions.create
-    
-    if ResponseFormat:
-        fn = ClientOpenai.beta.chat.completions.parse
-        
-    params = {
-         'model':"gemini-2.0-flash"
-        ,'n':1
-        ,'messages':messages
-        ,'response_format':ResponseFormat
-    }
-    
-    params.update(kwargs);
-    
-    response = fn(**params)
-    
-    if  params.get('stream'):
-        return response
-
-    return response.choices[0];
-
-def ai(system,user, schema, **kwargs):
-    msg = [
-         {'role':"system",'content':system}
-        ,{'role':"user",'content':user}
-     ] 
-    
-    return llm(msg, schema, **kwargs);
-    
-
-def search(text, top = 10, onConnectionError = None):
-    
-    EnglishText = text 
-    
-    embeddings = embed(text);
-    
-    query = f"""
-        declare @search vector(1024) = '{embeddings}'
-        
-        select top {top}
-            *
-        from (
-            select 
-                RelPath
-                ,Similaridade = 1-CosDistance
-                ,ScriptContent = ChunkContent
-                ,ContentLength = LEN(ChunkContent)
-                ,CosDistance
-            from
-                (
-                    select 
-                        *
-                        ,CosDistance = vector_distance('cosine',embeddings,@search)
-                    from 
-                        Scripts 
-                ) C
-        ) v
-        order by
-            CosDistance
-    """
-    
-    queryResults = sql(query, onConnectionError = onConnectionError);
-    
-
-    
-    return queryResults
-    
-    
-print("Loading embedding model");
-Embedder = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
-
-print("Loading reranker");
-Reranker = CrossEncoder("mixedbread-ai/mxbai-rerank-large-v1", activation_fn=nn.Sigmoid())
-
-class rfTranslatedText(BaseModel):
-    text: str = Field(description='Translated text')
-    lang: str = Field(description='source language')
-    
-class rfGenericText(BaseModel):
-    text: str = Field(description='The text result')
-
-def ChatFunc(message, history, LangMode, ChooseLang):
-    
-
-     # Determinar se o user quer fazer uma nova pesquisa!
-     IsNewSearch = True;
-     
-     messages = []
-     CurrentTable = None;
-     
-     def ChatBotOutput():
-         return [messages,CurrentTable]
-     
-     class BotMessage():
-          def __init__(self, *args, **kwargs):
-              self.Message      =  gr.ChatMessage(*args, **kwargs)
-              self.LastContent  = None 
-              messages.append(self.Message);
-              
-          def __call__(self, content, noNewLine = False):
-              if not content:
-                  return;
-              
-              self.Message.content += content;
-              self.LastContent = None;
-              
-              if not noNewLine:
-                  self.Message.content += "\n";
-              
-              return ChatBotOutput();
-              
-          def update(self,content):
-              
-              if not self.LastContent:
-                  self.LastContent = self.Message.content
-                  
-              self.Message.content = self.LastContent +" "+content+"\n";
-              
-              return ChatBotOutput(); 
-          
-          def done(self):
-              self.Message.metadata['status'] = "done";
-              return ChatBotOutput();
-     
-     def Reply(msg):
-         m = BotMessage(msg);
-         return ChatBotOutput();
-     
-     m = BotMessage("",metadata={"title":"Searching scripts...","status":"pending"});
-     
-     
-     def OnConnError(err):
-         print("Sql connection error:", err)
-     
-     
-     try:
-         # Responder algo sobre o historico!
-         if IsNewSearch:
-             
-             yield m("Enhancing the prompt...")
-             
-             LLMResult = ai("""
-                Translate the user's message to English.
-                The message is a question related to a SQL Server T-SQL script that the user is searching for.
-                You must do following actions:
-                    - Identify the language of user text, using BCP 47 code (example: pt-BR, en-US, ja-JP, etc.)
-                    - Generate translated user text to english
-                Return both source language and translated text.
-             """,message, rfTranslatedText)
-             Question = LLMResult.message.parsed.text;
-             
-             if LangMode == "auto":
-                SourceLang = LLMResult.message.parsed.lang;
-             else:
-                SourceLang = ChooseLang
-             
-             yield m(f"Lang:{SourceLang}({LangMode}), English Prompt: {Question}")
-                      
-             yield m("searching...")
-             try:
-                FoundScripts =  search(Question, onConnectionError = OnConnError)
-             except:
-                 yield m("Houve alguma falha ao executar a consulta no banco. Tente novamente. Se persistir, veja orientações na aba Help!")
-                 return;
-             
-             yield m("Doing rerank");
-             doclist = [doc['ScriptContent'] for doc in FoundScripts]
-            
-             # Faz o reranker!
-             for score in rerank(Question, doclist):
-                 i = score['corpus_id'];
-                 FoundScripts[i]['rank'] = str(score['score'])
-             
-             RankedScripts = sorted(FoundScripts, key=lambda item: float(item['rank']), reverse=True)
-
-             
-                
-             ScriptTable = []
-             for script in RankedScripts:
-                link = "https://github.com/rrg92/sqlserver-lib/tree/main/" + script['RelPath']
-                script['link'] = link;
-                
-                ScriptTable.append({
-                    'Link': f'<a title="{link}" href="{link}" target="_blank">{script["RelPath"]}</a>'
-                    ,'Length': script['ContentLength']
-                    ,'Cosine Similarity':  script['Similaridade']
-                    ,'Rank':  script['rank']
-                })
-             
-             
-             CurrentTable = pd.DataFrame(ScriptTable)
-             yield m("Found scripts, check Rank tab for details!")
-             
-             
-             WaitMessage = ai(f"""
-                You will analyze some T-SQL scripts in order to check which is best for the user.
-                You found scripts, presented them to the user, and now will do some work that takes time.
-                Generate a message to tell the user to wait while you work, in the same language as the user.
-                You will receive the question the user sent that triggered this process.
-                Use the user’s original question to customize the message.
-                Answer in lang: {SourceLang}
-             """,message,rfGenericText).message.parsed.text
-             
-             yield Reply(WaitMessage);
-             
-             yield m(f"Analyzing scripts...")
-             
-             
-             ResultJson = json.dumps(RankedScripts);
-             
-             SystemPrompt = f"""
-                You are an assistant that helps users find the best T-SQL scripts for their specific needs.  
-                These scripts were created by Rodrigo Ribeiro Gomes and are publicly available for users to query and use.
-
-                The user will provide a short description of what they are looking for, and your task is to present the most relevant scripts.
-
-                To assist you, here is a JSON object with the top matches based on the current user query:  
-                {ResultJson}
-                
-                ---
-                This JSON contains all the scripts that matched the user's input.  
-                Analyze each script's name and content, and create a ranked summary of the best recommendations according to the user's need.
-
-                Only use the information available in the provided JSON. Do not reference or mention anything outside of this list.  
-                You can include parts of the scripts in your answer to illustrate or give usage examples based on the user's request.
-
-                Re-rank the results if necessary, presenting them from the most to the least relevant.  
-                You may filter out scripts that appear unrelated to the user query.
-
-                ---
-                ### Output Rules
-
-                - Review each script and evaluate how well it matches the user’s request.  
-                - Summarize each script, ordering from the most relevant to the least relevant.  
-                - Write personalized and informative review text for each recommendation.  
-                - If applicable, explain how the user should run the script, including parameters or sections (like `WHERE` clauses) they might need to customize.  
-                - When referencing a script, include the link provided in the JSON — all scripts are hosted on GitHub
-                - YOU MUST ANSWER THAT LANGUAGE: {SourceLang}
-             """
-
-             ScriptPrompt = [
-                { 'role':'system', 'content':SystemPrompt }
-                ,{ 'role':'user', 'content':message }
-             ]
-            
-             
-
-
-             llmanswer = llm(ScriptPrompt, stream = True) 
-             yield m.done()
-             
-             answer = BotMessage("");
-             
-             for chunk in llmanswer:
-                 content = chunk.choices[0].delta.content
-                 yield answer(content, noNewLine = True)
-     finally:          
-        yield m.done()
-
-def SearchFiles(message):
-    
-    Question = message;
-    
-    try:
-        FoundScripts =  search(Question)
-    except:
-        return m("Houve alguma falha ao executar a consulta no banco. Tente novamente. Se persistir, veja orientações na aba Help!")
-        return;
-        
-    doclist = [doc['ScriptContent'] for doc in FoundScripts]
-
-    # Faz o reranker!
-
-             
-             
-    ScriptTable = [];
-    for score in rerank(Question, doclist):
-        i = score['corpus_id'];
-        script =  FoundScripts[i];
-        script['rank'] = str(score['score'])
-        link = "https://github.com/rrg92/sqlserver-lib/tree/main/" + script['RelPath']
-        script['link'] = link;
-        
-        if not AsJson:
-                ScriptTable.append({
-                    'Link': f'<a title="{link}" href="{link}" target="_blank">{script["RelPath"]}</a>'
-                    ,'Length': script['ContentLength']
-                    ,'Cosine Similarity':  script['Similaridade']
-                    ,'Rank':  script['rank']
-                })
-
-    RankedScripts = sorted(FoundScripts, key=lambda item: float(item['rank']), reverse=True)
-    
-    #result = pd.DataFrame(ScriptTable)
-    jsonresult = json.dumps(RankedScripts)
-    
-    return jsonresult;
-
-resultTable = gr.Dataframe(datatype = ['html','number','number'], interactive = False, show_search = "search");
-TextResults = gr.Textbox()
-
-with gr.Blocks(fill_height=True) as demo:
-    
-    with gr.Column():
-        
-        tabSettings = gr.Tab("Settings", render = False)
-        
-        with tabSettings:
-            LangOpts = gr.Radio([("Auto Detect from text","auto"), ("Use browser language","browser")], value="auto", label="Language", info="Choose lang used by AI to answer you!")
-            LangChoose = gr.Textbox(info = "This will be filled with detect browser language, but you can change")
-            
-            LangOpts.change(None, [LangOpts],[LangChoose], js = """
-                function(opt){
-                    if(opt == "browser"){
-                        return navigator ? navigator.language : "en-US";
-                    }
-                }
-            """)
-
-        
-        with gr.Tab("Chat", scale = 1):
-            ChatTextBox = gr.Textbox(max_length = 500, info = "Which script are you looking for?", submit_btn = True);
-            
-            gr.ChatInterface(
-                ChatFunc
-                ,additional_outputs=[resultTable]
-                ,additional_inputs=[LangOpts,LangChoose]
-                ,type="messages"
-                ,textbox = ChatTextBox
-            ) 
-
-        tabSettings.render()
-            
-            
-        with gr.Tab("Rank"):
-            txtSearchTable = gr.Textbox(label="Search script files",info="Description of what you want", visible = False)
-            AsJson = gr.Checkbox(visible = False)
-            resultTable.render();
-            
-            
-            txtSearchTable.submit(SearchFiles, [txtSearchTable],[TextResults])
-            
-        with gr.Tab("Help"):
-            gr.Markdown("""
-                Bem-vindo ao Space SQL Server Lib  
-                Este space permite que você encontre scripts SQL do https://github.com/rrg92/sqlserver-lib com base nas suas necessidades  
-                
-
-                ## Instruções de Uso
-                Apenas descreva o que você precisa no campo de chat e aguarde a IA analisar os melhores scripts do repositório para você.  
-                Além de uma explicação feita pela IA, a aba "Rank", contém uma tabela com os scripts encontrados e seus respectictos rank. 
-                A coluna Cosine Similarity é o nível de similaridades da sua pergunta com o script (calculado baseado nos embeddings do seu texto e do script).
-                A coluna Rank é um score onde quanto maior o valor mais relacionado ao seu texto o script é (calculado usando rerank/cross encoders). A tabela vem ordenada por essa coluna.
-            
-                    
-                ## Fluxo básico  
-                - Quando você digita o texto, iremos fazer uma busca usando embeddings em um banco Azure SQL Database  
-                - Os embeddings são calculados usando um modelo carregado no proprio script, via ZeroGPU.  
-                - Os top 20 resultados mais similares são retornados e então um rerank é feito  
-                - O rerank também é feito por um modelo que roda no próprio script, em ZeroGPU  
-                - Estes resultados ordenados por reran, são então enviados ao LLM para que analise e monte uma resposta para você.  
-
-                
-                ## Sobre o uso e eventuais erros 
-                Eu tento usar o máximo de recursos FREE e open possíveis, e portanto, eventualmente, o Space pode falhar por alguma limitação.   
-                Alguns possíveis pontos de falha:
-                - Créditos free do google ou rate limit 
-                - Azure SQL database offline devido a crédito ou ao auto-pause (devido ao free tier)
-                - Limites de uso do ZeroGPU do Hugging Face.
-                    
-                Você pode me procurar no [linkedin](https://www.linkedin.com/in/rodrigoribeirogomes/), caso receba erroslimit 
-                
-            """)
-        
-        with gr.Tab("Other", visible = False):
-            txtEmbed  = gr.Text(label="Text to embed", visible=False)
-            btnEmbed = gr.Button("embed");
-            btnEmbed.click(embed, [txtEmbed], [txtEmbed])
-            
-            TextResults.render();
-       
-        
-        
-        
-    
-    
-    
-    
-    
-    
-
-
-
-if __name__ == "__main__":
-    demo.launch(
-        share=False,
-        debug=False,
-        server_port=7860,
-        server_name="0.0.0.0",
-        allowed_paths=[]
-    )
+import gradio as gr
+import spaces
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+from sentence_transformers.quantization import quantize_embeddings
+import pymssql
+import os
+import pandas as pd
+from openai import OpenAI
+from pydantic import BaseModel, Field
+import json
+from sentence_transformers import CrossEncoder
+from torch import nn
+import time
+
+
+SqlServer = os.environ['SQL_SERVER']
+SqlDatabase = os.environ['SQL_DB']
+SqlUser = os.environ['SQL_USER']
+SqlPass = os.environ['SQL_PASS']
+
+
+OpenaiApiKey = os.environ.get("OPENAI_API_KEY")
+OpenaiBaseUrl = os.environ.get("OPENAI_BASE_URL","https://generativelanguage.googleapis.com/v1beta/openai")
+
+
+def sql(query,db=SqlDatabase, login_timeout = 120,onConnectionError = None):
+    
+    start_time = time.time()
+    
+    while True:
+        try:
+            cnxn = pymssql.connect(SqlServer,SqlUser,SqlPass,db, login_timeout = 5)
+            break;
+        except Exception as e:
+            if onConnectionError:
+                onConnectionError(e)
+                
+            if time.time() - start_time > login_timeout:
+                raise TimeoutError("SQL Connection Timeout");
+                
+            time.sleep(1)  # Espera 1 segundo antes de tentar novamente
+    
+    
+    cursor = cnxn.cursor()
+    cursor.execute(query)
+    
+    columns = [column[0] for column in cursor.description]
+    results = [dict(zip(columns, row)) for row in cursor.fetchall()]
+    
+    return results;
+
+
+
+@spaces.GPU
+def embed(text):
+        
+    query_embedding = Embedder.encode(text)
+    return query_embedding.tolist();
+
+ 
+@spaces.GPU 
+def rerank(query,documents, **kwargs):
+    return Reranker.rank(query, documents, **kwargs)
+
+ClientOpenai = OpenAI(
+    api_key=OpenaiApiKey 
+    ,base_url=OpenaiBaseUrl 
+)
+
+def llm(messages, ResponseFormat = None, **kwargs):
+    
+    fn = ClientOpenai.chat.completions.create
+    
+    if ResponseFormat:
+        fn = ClientOpenai.beta.chat.completions.parse
+        
+    params = {
+         'model':"gemini-2.0-flash"
+        ,'n':1
+        ,'messages':messages
+        ,'response_format':ResponseFormat
+    }
+    
+    params.update(kwargs);
+    
+    response = fn(**params)
+    
+    if  params.get('stream'):
+        return response
+
+    return response.choices[0];
+
+def ai(system,user, schema, **kwargs):
+    msg = [
+         {'role':"system",'content':system}
+        ,{'role':"user",'content':user}
+     ] 
+    
+    return llm(msg, schema, **kwargs);
+    
+
+def search(text, top = 10, onConnectionError = None):
+    
+    EnglishText = text 
+    
+    embeddings = embed(text);
+    
+    query = f"""
+        declare @search vector(1024) = '{embeddings}'
+        
+        select top {top}
+            *
+        from (
+            select 
+                RelPath
+                ,Similaridade = 1-CosDistance
+                ,ScriptContent = ChunkContent
+                ,ContentLength = LEN(ChunkContent)
+                ,CosDistance
+            from
+                (
+                    select 
+                        *
+                        ,CosDistance = vector_distance('cosine',embeddings,@search)
+                    from 
+                        Scripts 
+                ) C
+        ) v
+        order by
+            CosDistance
+    """
+    
+    queryResults = sql(query, onConnectionError = onConnectionError);
+    
+
+    
+    return queryResults
+    
+    
+print("Loading embedding model");
+Embedder = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+
+print("Loading reranker");
+Reranker = CrossEncoder("mixedbread-ai/mxbai-rerank-large-v1", activation_fn=nn.Sigmoid())
+
+class rfTranslatedText(BaseModel):
+    text: str = Field(description='Translated text')
+    lang: str = Field(description='source language')
+    
+class rfGenericText(BaseModel):
+    text: str = Field(description='The text result')
+
+def ChatFunc(message, history, LangMode, ChooseLang):
+    
+
+     # Determinar se o user quer fazer uma nova pesquisa!
+     IsNewSearch = True;
+     
+     messages = []
+     CurrentTable = None;
+     
+     def ChatBotOutput():
+         return [messages,CurrentTable]
+     
+     class BotMessage():
+          def __init__(self, *args, **kwargs):
+              self.Message      =  gr.ChatMessage(*args, **kwargs)
+              self.LastContent  = None 
+              messages.append(self.Message);
+              
+          def __call__(self, content, noNewLine = False):
+              if not content:
+                  return;
+              
+              self.Message.content += content;
+              self.LastContent = None;
+              
+              if not noNewLine:
+                  self.Message.content += "\n";
+              
+              return ChatBotOutput();
+              
+          def update(self,content):
+              
+              if not self.LastContent:
+                  self.LastContent = self.Message.content
+                  
+              self.Message.content = self.LastContent +" "+content+"\n";
+              
+              return ChatBotOutput(); 
+          
+          def done(self):
+              self.Message.metadata['status'] = "done";
+              return ChatBotOutput();
+     
+     def Reply(msg):
+         m = BotMessage(msg);
+         return ChatBotOutput();
+     
+     m = BotMessage("",metadata={"title":"Searching scripts...","status":"pending"});
+     
+     
+     def OnConnError(err):
+         print("Sql connection error:", err)
+     
+     
+     try:
+         # Responder algo sobre o historico!
+         if IsNewSearch:
+             
+             yield m("Enhancing the prompt...")
+             
+             LLMResult = ai("""
+                Translate the user's message to English.
+                The message is a question related to a SQL Server T-SQL script that the user is searching for.
+                You must do following actions:
+                    - Identify the language of user text, using BCP 47 code (example: pt-BR, en-US, ja-JP, etc.)
+                    - Generate translated user text to english
+                Return both source language and translated text.
+             """,message, rfTranslatedText)
+             Question = LLMResult.message.parsed.text;
+             
+             if LangMode == "auto":
+                SourceLang = LLMResult.message.parsed.lang;
+             else:
+                SourceLang = ChooseLang
+             
+             yield m(f"Lang:{SourceLang}({LangMode}), English Prompt: {Question}")
+                      
+             yield m("searching...")
+             try:
+                FoundScripts =  search(Question, onConnectionError = OnConnError)
+             except:
+                 print('Search Error:')
+                 print(e)
+                 yield m("Houve alguma falha ao fazer a pesquisa. Tente novamente. Se persistir, veja orientações na aba Help!")
+                 return;
+             
+             yield m("Doing rerank");
+             doclist = [doc['ScriptContent'] for doc in FoundScripts]
+            
+             # Faz o reranker!
+             for score in rerank(Question, doclist):
+                 i = score['corpus_id'];
+                 FoundScripts[i]['rank'] = str(score['score'])
+             
+             RankedScripts = sorted(FoundScripts, key=lambda item: float(item['rank']), reverse=True)
+
+             
+                
+             ScriptTable = []
+             for script in RankedScripts:
+                link = "https://github.com/rrg92/sqlserver-lib/tree/main/" + script['RelPath']
+                script['link'] = link;
+                
+                ScriptTable.append({
+                    'Link': f'<a title="{link}" href="{link}" target="_blank">{script["RelPath"]}</a>'
+                    ,'Length': script['ContentLength']
+                    ,'Cosine Similarity':  script['Similaridade']
+                    ,'Rank':  script['rank']
+                })
+             
+             
+             CurrentTable = pd.DataFrame(ScriptTable)
+             yield m("Found scripts, check Rank tab for details!")
+             
+             
+             WaitMessage = ai(f"""
+                You will analyze some T-SQL scripts in order to check which is best for the user.
+                You found scripts, presented them to the user, and now will do some work that takes time.
+                Generate a message to tell the user to wait while you work, in the same language as the user.
+                You will receive the question the user sent that triggered this process.
+                Use the user’s original question to customize the message.
+                Answer in lang: {SourceLang}
+             """,message,rfGenericText).message.parsed.text
+             
+             yield Reply(WaitMessage);
+             
+             yield m(f"Analyzing scripts...")
+             
+             
+             ResultJson = json.dumps(RankedScripts);
+             
+             SystemPrompt = f"""
+                You are an assistant that helps users find the best T-SQL scripts for their specific needs.  
+                These scripts were created by Rodrigo Ribeiro Gomes and are publicly available for users to query and use.
+
+                The user will provide a short description of what they are looking for, and your task is to present the most relevant scripts.
+
+                To assist you, here is a JSON object with the top matches based on the current user query:  
+                {ResultJson}
+                
+                ---
+                This JSON contains all the scripts that matched the user's input.  
+                Analyze each script's name and content, and create a ranked summary of the best recommendations according to the user's need.
+
+                Only use the information available in the provided JSON. Do not reference or mention anything outside of this list.  
+                You can include parts of the scripts in your answer to illustrate or give usage examples based on the user's request.
+
+                Re-rank the results if necessary, presenting them from the most to the least relevant.  
+                You may filter out scripts that appear unrelated to the user query.
+
+                ---
+                ### Output Rules
+
+                - Review each script and evaluate how well it matches the user’s request.  
+                - Summarize each script, ordering from the most relevant to the least relevant.  
+                - Write personalized and informative review text for each recommendation.  
+                - If applicable, explain how the user should run the script, including parameters or sections (like `WHERE` clauses) they might need to customize.  
+                - When referencing a script, include the link provided in the JSON — all scripts are hosted on GitHub
+                - YOU MUST ANSWER THAT LANGUAGE: {SourceLang}
+             """
+
+             ScriptPrompt = [
+                { 'role':'system', 'content':SystemPrompt }
+                ,{ 'role':'user', 'content':message }
+             ]
+            
+             
+
+
+             llmanswer = llm(ScriptPrompt, stream = True) 
+             yield m.done()
+             
+             answer = BotMessage("");
+             
+             for chunk in llmanswer:
+                 content = chunk.choices[0].delta.content
+                 yield answer(content, noNewLine = True)
+     finally:          
+        yield m.done()
+
+def SearchFiles(message):
+    
+    Question = message;
+    
+    try:
+        FoundScripts =  search(Question)
+    except:
+        return m("Houve alguma falha ao executar a consulta no banco. Tente novamente. Se persistir, veja orientações na aba Help!")
+        return;
+        
+    doclist = [doc['ScriptContent'] for doc in FoundScripts]
+
+    # Faz o reranker!
+
+             
+             
+    ScriptTable = [];
+    for score in rerank(Question, doclist):
+        i = score['corpus_id'];
+        script =  FoundScripts[i];
+        script['rank'] = str(score['score'])
+        link = "https://github.com/rrg92/sqlserver-lib/tree/main/" + script['RelPath']
+        script['link'] = link;
+        
+        if not AsJson:
+                ScriptTable.append({
+                    'Link': f'<a title="{link}" href="{link}" target="_blank">{script["RelPath"]}</a>'
+                    ,'Length': script['ContentLength']
+                    ,'Cosine Similarity':  script['Similaridade']
+                    ,'Rank':  script['rank']
+                })
+
+    RankedScripts = sorted(FoundScripts, key=lambda item: float(item['rank']), reverse=True)
+    
+    #result = pd.DataFrame(ScriptTable)
+    jsonresult = json.dumps(RankedScripts)
+    
+    return jsonresult;
+
+resultTable = gr.Dataframe(datatype = ['html','number','number'], interactive = False, show_search = "search");
+TextResults = gr.Textbox()
+
+with gr.Blocks(fill_height=True) as demo:
+    
+    with gr.Column():
+        
+        tabSettings = gr.Tab("Settings", render = False)
+        
+        with tabSettings:
+            LangOpts = gr.Radio([("Auto Detect from text","auto"), ("Use browser language","browser")], value="auto", label="Language", info="Choose lang used by AI to answer you!")
+            LangChoose = gr.Textbox(info = "This will be filled with detect browser language, but you can change")
+            
+            LangOpts.change(None, [LangOpts],[LangChoose], js = """
+                function(opt){
+                    if(opt == "browser"){
+                        return navigator ? navigator.language : "en-US";
+                    }
+                }
+            """)
+
+        
+        with gr.Tab("Chat", scale = 1):
+            ChatTextBox = gr.Textbox(max_length = 500, info = "Which script are you looking for?", submit_btn = True);
+            
+            gr.ChatInterface(
+                ChatFunc
+                ,additional_outputs=[resultTable]
+                ,additional_inputs=[LangOpts,LangChoose]
+                ,type="messages"
+                ,textbox = ChatTextBox
+            ) 
+
+        tabSettings.render()
+            
+            
+        with gr.Tab("Rank"):
+            txtSearchTable = gr.Textbox(label="Search script files",info="Description of what you want", visible = False)
+            AsJson = gr.Checkbox(visible = False)
+            resultTable.render();
+            
+            
+            txtSearchTable.submit(SearchFiles, [txtSearchTable],[TextResults])
+            
+        with gr.Tab("Help"):
+            gr.Markdown("""
+                Bem-vindo ao Space SQL Server Lib  
+                Este space permite que você encontre scripts SQL do https://github.com/rrg92/sqlserver-lib com base nas suas necessidades  
+                
+
+                ## Instruções de Uso
+                Apenas descreva o que você precisa no campo de chat e aguarde a IA analisar os melhores scripts do repositório para você.  
+                Além de uma explicação feita pela IA, a aba "Rank", contém uma tabela com os scripts encontrados e seus respectictos rank. 
+                A coluna Cosine Similarity é o nível de similaridades da sua pergunta com o script (calculado baseado nos embeddings do seu texto e do script).
+                A coluna Rank é um score onde quanto maior o valor mais relacionado ao seu texto o script é (calculado usando rerank/cross encoders). A tabela vem ordenada por essa coluna.
+            
+                    
+                ## Fluxo básico  
+                - Quando você digita o texto, iremos fazer uma busca usando embeddings em um banco Azure SQL Database  
+                - Os embeddings são calculados usando um modelo carregado no proprio script, via ZeroGPU.  
+                - Os top 20 resultados mais similares são retornados e então um rerank é feito  
+                - O rerank também é feito por um modelo que roda no próprio script, em ZeroGPU  
+                - Estes resultados ordenados por reran, são então enviados ao LLM para que analise e monte uma resposta para você.  
+
+                
+                ## Sobre o uso e eventuais erros 
+                Eu tento usar o máximo de recursos FREE e open possíveis, e portanto, eventualmente, o Space pode falhar por alguma limitação.   
+                Alguns possíveis pontos de falha:
+                - Créditos free do google ou rate limit 
+                - Azure SQL database offline devido a crédito ou ao auto-pause (devido ao free tier)
+                - Limites de uso do ZeroGPU do Hugging Face.
+                    
+                Você pode me procurar no [linkedin](https://www.linkedin.com/in/rodrigoribeirogomes/), caso receba erroslimit 
+                
+            """)
+        
+        with gr.Tab("Other", visible = False):
+            txtEmbed  = gr.Text(label="Text to embed", visible=False)
+            btnEmbed = gr.Button("embed");
+            btnEmbed.click(embed, [txtEmbed], [txtEmbed])
+            
+            TextResults.render();
+       
+        
+        
+        
+    
+    
+    
+    
+    
+    
+
+
+
+if __name__ == "__main__":
+    demo.launch(
+        share=False,
+        debug=False,
+        server_port=7860,
+        server_name="0.0.0.0",
+        allowed_paths=[]
+    )