new model

app.py

@@ -1,76 +1,110 @@
-import gradio as gr
-import pandas as pd
-import nltk
-from nltk.corpus import stopwords
-from sentence_transformers import SentenceTransformer, util
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-
 from unsloth import FastLanguageModel
 from peft import PeftModel
-from unsloth.chat_templates import get_chat_template
 
-# Download NLTK stopwords if not already downloaded
-nltk.download("stopwords")
-
-# 1. Load model + tokenizer
-base_model_name = "unsloth/Llama-3.2-3B-Instruct"
+# Load the base model with FastLanguageModel
 model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name=base_model_name,
+    model_name="unsloth/Llama-3.2-3B-Instruct",
     max_seq_length=2048,
     dtype=None,
     load_in_4bit=True
 )
 
-# 2. Load the LoRA adapter
-adapter_path = "jaspersands/model"  # Adjust if needed
+base_model_name = "unsloth/Llama-3.2-3B-Instruct"
+adapter_path = "jaspersands/model"  # Path to LoRA adapter on Hugging Face
+
 model = PeftModel.from_pretrained(model, adapter_path)
 
-# 3. Load data
-file_path = "Clean Missouri Data.csv"  # Ensure this CSV is in your repo
-df = pd.read_csv(file_path, encoding="MacRoman")
+# Code for processing a query
+import pandas as pd
+from unsloth.chat_templates import get_chat_template
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from sentence_transformers import SentenceTransformer, util
+import nltk
+
+# Ensure you have NLTK stopwords downloaded
+nltk.download("stopwords")
+from nltk.corpus import stopwords
+
+# Step 1: Load the CSV file
+file_path = '/content/Clean Missouri Data.csv'
+df = pd.read_csv(file_path, encoding='MacRoman')
 
-# 4. Define helper functions
-def search_relevant_policies(query, df, top_n=10):
+# Step 2: Define a function to search relevant policies based on the user's query using cosine similarity
+def search_relevant_policies(query, df, top_n=10, max_chars = 40000):
+    # Convert policies into a TF-IDF matrix
     tfidf = TfidfVectorizer(stop_words='english')
     tfidf_matrix = tfidf.fit_transform(df['Content'])
+
+    # Get the query as a TF-IDF vector
     query_vector = tfidf.transform([query])
+
+    # Calculate cosine similarity between query and policies
     cosine_sim = cosine_similarity(query_vector, tfidf_matrix).flatten()
+
+    # Get the top N relevant policies
+    top_indices = cosine_sim.argsort()[-top_n:][::-1]
+    relevant_policies = df.iloc[top_indices]
+
     top_indices = cosine_sim.argsort()[-top_n:][::-1]
-    return df.iloc[top_indices]
+    relevant_policies = df.iloc[top_indices].copy()
+
+    # Ensure total text is capped at max_chars
+    char_count = 0
+    valid_indices = []
+
+    for idx, row in relevant_policies.iterrows():
+        content_length = len(row["Content"])
+
+        # If adding this content exceeds max_chars, stop adding any further policies
+        if char_count + content_length > max_chars:
+            break
+
+        # Otherwise, keep this policy
+        char_count += content_length
+        valid_indices.append(idx)
+
+    # Filter the dataframe to only include valid rows
+    truncated_policies = relevant_policies.loc[valid_indices]
+
+    return truncated_policies
+
 
 def get_content_after_query(response_text, query):
+    # Find the position of the query within the response text
     query_position = response_text.lower().find(query.lower())
     if query_position != -1:
+        # Return the content after the query position
         res = response_text[query_position + len(query):].strip()
         return res[11:]
     else:
+        # If the query is not found, return the full response text as a fallback
         return response_text.strip()
 
-def process_query(query, tokenizer):
-    # 1. Get relevant policies
+
+def process_query(query,tokenizer):
+
     relevant_policies = search_relevant_policies(query, df)
 
-    # 2. Format relevant policies
+    # Step 5: Combine the relevant policies with the user's query for the model
     formatted_policies = []
     for index, row in relevant_policies.iterrows():
-        formatted_policy = (
-            f"Title: {row['Title']}\nTerritory: {row['Territory']}\n"
-            f"Type: {row['Type']}\nYear: {row['Year']}\nCategory: {row['Category']}\n"
-            f"From: {row['From']}\nTo: {row['To']}\nContent: {row['Content']}\n"
-            f"Link: {row['Link to Content']}\n"
-        )
-        formatted_policies.append(formatted_policy)
+        # formatted_policy = f"Title: {row['Title']}\nTerritory: {row['Territory']}\nType: {row['Type']}\nYear: {row['Year']}\nCategory: {row['Category']}\nFrom: {row['From']}\nTo: {row['To']}\nContent: {row['Content']}\nLink: {row['Link to Content']}\n"
+        # formatted_policies.append(formatted_policy)
+        formatted_policies.append(row['Content'])
     relevant_policy_text = "\n\n".join(formatted_policies)
 
-    # 3. Create messages for model
+    # Messages with relevant policies for the model
     messages_with_relevant_policies = [
         {"role": "system", "content": relevant_policy_text},
         {"role": "user", "content": query},
     ]
 
-    # 4. Tokenize with chat template
-    tokenizer = get_chat_template(tokenizer, chat_template="llama-3.1")
+    # Step 6: Apply chat template and tokenize
+    tokenizer = get_chat_template(
+        tokenizer,
+        chat_template="llama-3.1",
+    )
     inputs = tokenizer.apply_chat_template(
         messages_with_relevant_policies,
         tokenize=True,
@@ -78,43 +112,109 @@ def process_query(query, tokenizer):
         return_tensors="pt"
     ).to("cuda")
 
-    # 5. Generate output
     FastLanguageModel.for_inference(model)
-    outputs = model.generate(
-        input_ids=inputs,
-        max_new_tokens=256,
-        use_cache=True,
-        temperature=1.5,
-        min_p=0.1
-    )
+    outputs = model.generate(input_ids=inputs, max_new_tokens=512, use_cache=True, temperature=0.7, min_p=0.1)
+
+    # Step 7: Decode the output
     generated_response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
     response = get_content_after_query(generated_response, query)
 
-    # 6. Rank the top 10 policies using SBERT
-    model_sbert = SentenceTransformer("all-MiniLM-L6-v2")
+    # Step 8: Rank the top 10 policies using SBERT for the final link
+    # Load SBERT model
+    model_sbert = SentenceTransformer('all-MiniLM-L6-v2')  # You can choose another SBERT model if desired
+
+    # Encode the generated response using SBERT
     response_embedding = model_sbert.encode(generated_response, convert_to_tensor=True)
+
+    # Encode each policy in the top 10 list
     policy_embeddings = model_sbert.encode(relevant_policies['Content'].tolist(), convert_to_tensor=True)
+
+    # Calculate cosine similarities between the generated response and each policy embedding
     cosine_similarities = util.cos_sim(response_embedding, policy_embeddings).flatten()
+
+    # Identify the policy with the highest SBERT cosine similarity score
     most_relevant_index = cosine_similarities.argmax().item()
     most_relevant_link = relevant_policies.iloc[most_relevant_index]['Link to Content']
 
-    return {"response": response, "most_relevant_link": most_relevant_link}
-
-# 5. Gradio interface
-def answer_query(u_query):
-    result = process_query(u_query, tokenizer)
-    return result["response"], result["most_relevant_link"]
-
-demo = gr.Interface(
-    fn=answer_query,
-    inputs="text",
-    outputs=[
-        gr.Textbox(label="System Response"),
-        gr.Textbox(label="Relevant Link")
-    ],
-    title="Foster Questions",
-    description="Enter your question about the US foster system"
-)
+    # Print the link to the most relevant source
+    return {
+        "response": response,
+        "most_relevant_link": most_relevant_link
+    }
+
+
+    # Load Google Sheets to store results
+import json
+from google.oauth2.service_account import Credentials
+import gspread
+import pandas as pd
+
+# Load the service account JSON
+json_file_path = "fostercare-449201-75a303a8c238.json"  # Load the credentials for the service account
+with open(json_file_path, 'r') as file:
+    service_account_data = json.load(file)
+
+# Authenticate using the loaded service account data
+scopes = ["https://www.googleapis.com/auth/spreadsheets", "https://www.googleapis.com/auth/drive"]
+creds = Credentials.from_service_account_info(service_account_data, scopes=scopes)
+client = gspread.authorize(creds)
+
+# Open the shared Google Sheet by name
+spreadsheet = client.open("Foster Care RA Responses").sheet1
+
+# Link to Google Sheet
+# https://docs.google.com/spreadsheets/d/15iEcxmTgkgfcxzDGnq3i_nP1hiAXgb3RplHgqAMEyHA/edit?usp=sharing
+
+
+# Code to set up Gradio GUI
+import gradio as gr
+
+def greet(query):
+    result_1 = process_query(query, tokenizer)
+    content_after_query_1 = result_1["response"]
+
+    result_2 = process_query(query, tokenizer)
+    content_after_query_2 = result_2["response"]
+
+    return [content_after_query_1, content_after_query_2]
+
+def choose_preference(name, output1, output2, preference, query):
+    if not name:
+      return "Please enter your name before submitting."
+
+    if preference == "Output 1":
+        new_row = [query, output1, output2, name]
+        spreadsheet.append_row(new_row)
+        return f"You preferred: Output 1 - {output1}"
+    elif preference == "Output 2":
+        new_row = [query, output2, output1, name]
+        spreadsheet.append_row(new_row)
+        return f"You preferred: Output 2 - {output2}"
+    else:
+        return "No preference selected."
+
+# Define the interface
+with gr.Blocks() as demo:
+    # Name input
+    name_input = gr.Textbox(label="Enter your name")
+
+    # Input for query
+    query_input = gr.Textbox(label="Enter your query")
+
+    # Outputs
+    output_1 = gr.Textbox(label="Output 1", interactive=False)
+    output_2 = gr.Textbox(label="Output 2", interactive=False)
+
+    # Preference selection
+    preference = gr.Radio(["Output 1", "Output 2"], label="Choose your preferred output")
+    preference_result = gr.Textbox(label="Your Preference", interactive=False)
+
+    # Buttons
+    generate_button = gr.Button("Generate Outputs")
+    submit_button = gr.Button("Submit Preference")
+
+    # Link actions to buttons
+    generate_button.click(greet, inputs=query_input, outputs=[output_1, output_2])
+    submit_button.click(choose_preference, inputs=[name_input, output_1, output_2, preference, query_input], outputs=preference_result)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()

requirements.txt

@@ -1,8 +1,9 @@
-# requirements.txt
+torch
+torchvision
 unsloth
-peft
-gradio
 scikit-learn
 pandas
 nltk
-sentence-transformers
+sentence-transformers
+gradio
+peft