Update app.py

app.py

@@ -17,23 +17,17 @@ from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
 from langchain_community.vectorstores import FAISS
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 
-# ======================
-# SECRETS CONFIGURATION
-# ======================
-# Get API keys from Hugging Face Secrets
 GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
 HUGGINGFACE_ACCESS_TOKEN = os.environ.get("HUGGINGFACE_ACCESS_TOKEN")
 
-# Validate required secrets
 if not GOOGLE_API_KEY:
-    st.error("❌ GOOGLE_API_KEY not found. Please set it in Space Settings > Secrets.")
+    st.error("❌ GOOGLE_API_KEY not found.")
     st.stop()
 
 if not HUGGINGFACE_ACCESS_TOKEN:
-    st.error("❌ HUGGINGFACE_ACCESS_TOKEN not found. Please set it in Space Settings > Secrets.")
+    st.error("❌ HUGGINGFACE_ACCESS_TOKEN not found.")
     st.stop()
 
-# Initialize LLM and embeddings with secrets
 llm = ChatGoogleGenerativeAI(
     model="gemini-1.5-pro",
     google_api_key=GOOGLE_API_KEY
@@ -44,9 +38,6 @@ embeddings = HuggingFaceInferenceAPIEmbeddings(
     model_name="BAAI/bge-small-en-v1.5"
 )
 
-# ======================
-# DOCUMENT ANALYSIS CLASSES
-# ======================
 class KeyPoint(BaseModel):
     point: str = Field(description="A key point extracted from the document.")
 
@@ -57,9 +48,6 @@ class DocumentAnalysis(BaseModel):
     key_points: List[KeyPoint] = Field(description="List of key points from the document.")
     summary: Summary = Field(description="Summary of the document.")
 
-# ======================
-# CHAIN SETUP
-# ======================
 parser = PydanticOutputParser(pydantic_object=DocumentAnalysis)
 
 prompt_template = """
@@ -75,9 +63,6 @@ prompt = PromptTemplate(
 
 chain = LLMChain(llm=llm, prompt=prompt, output_parser=parser)
 
-# ======================
-# UTILITY FUNCTIONS
-# ======================
 def analyze_text_structured(text):
     return chain.run(text=text)
 
@@ -99,7 +84,7 @@ def create_pdf_report(analysis):
     pdf.cell(200, 10, txt="PDF Analysis Report", ln=True, align='C')
     pdf.cell(200, 10, txt=f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True, align='C')
     pdf.multi_cell(0, 10, txt=json_to_text(analysis))
-    return pdf.output(dest='S')
+    return bytes(pdf.output(dest='S').encode('latin-1'))
 
 def create_word_report(analysis):
     doc = Document()
@@ -112,9 +97,6 @@ def create_word_report(analysis):
     docx_bytes.seek(0)
     return docx_bytes.getvalue()
 
-# ======================
-# STREAMLIT UI
-# ======================
 st.set_page_config(page_title="Chat With PDF", page_icon="📄")
 
 def local_css():
@@ -201,7 +183,6 @@ def local_css():
 
 local_css()
 
-# Initialize session state
 if "current_file" not in st.session_state:
     st.session_state.current_file = None
 if "pdf_summary" not in st.session_state:
@@ -217,14 +198,12 @@ if "vectorstore" not in st.session_state:
 if "messages" not in st.session_state:
     st.session_state.messages = []
 
-# UI Components
 st.markdown('<div class="main-header">', unsafe_allow_html=True)
 st.markdown('<div class="flag-stripe"></div>', unsafe_allow_html=True)
 st.title("📄 Chat With PDF")
 st.caption("Your AI-powered Document Analyzer")
 st.markdown('</div>', unsafe_allow_html=True)
 
-# File Uploader
 with st.container():
     st.markdown('<div class="card animate-fadeIn">', unsafe_allow_html=True)
     uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
@@ -274,8 +253,7 @@ with st.container():
                 )
     st.markdown('</div>', unsafe_allow_html=True)
 
-# Chat Interface
-if "vectorstore" in st.session_state:
+if "vectorstore" in st.session_state and st.session_state.vectorstore is not None:
     st.subheader("Chat with the Document")
     
     for message in st.session_state.messages:
@@ -303,7 +281,6 @@ if "vectorstore" in st.session_state:
         
         st.session_state.messages.append({"role": "assistant", "content": response.content})
 
-# Footer
 st.markdown(
     f'<div class="footer">Analysis Time: {st.session_state.analysis_time:.1f}s | Powered by Google Generative AI</div>',
     unsafe_allow_html=True