Create app.py

app.py

@@ -0,0 +1,107 @@
+import gradio as gr
+import os
+from langchain_community.document_loaders import PyPDFLoader, UnstructuredPDFLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.chat_models import ChatOpenAI
+
+def process_pdf(file_path):
+    """Process PDF with fallback strategies"""
+    try:
+        # Try different loaders with fallback
+        try:
+            loader = PyPDFLoader(file_path)
+            documents = loader.load()
+        except:
+            loader = UnstructuredPDFLoader(file_path, strategy="ocr_only")
+            documents = loader.load()
+        
+        # Create embeddings and vector store
+        embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+        return Chroma.from_documents(documents, embeddings)
+    except Exception as e:
+        raise gr.Error(f"Error processing PDF: {str(e)}")
+
+def setup_conversation_chain(vector_store, api_key):
+    """Initialize conversation chain with memory"""
+    try:
+        os.environ["OPENAI_API_KEY"] = api_key
+        memory = ConversationBufferMemory(
+            memory_key="chat_history",
+            return_messages=True
+        )
+        return ConversationalRetrievalChain.from_llm(
+            ChatOpenAI(temperature=0.1),
+            vector_store.as_retriever(search_kwargs={"k": 3}),
+            memory=memory
+        )
+    except Exception as e:
+        raise gr.Error(f"Error initializing chat: {str(e)}")
+
+def upload_file(file, api_key, chat_history):
+    """Handle PDF upload and initialization"""
+    if not api_key.startswith("sk-"):
+        raise gr.Error("Invalid OpenAI API key format")
+    
+    if not file.name.endswith('.pdf'):
+        raise gr.Error("Only PDF files are supported")
+    
+    vector_store = process_pdf(file.name)
+    if not vector_store:
+        raise gr.Error("Failed to process PDF")
+    
+    conversation_chain = setup_conversation_chain(vector_store, api_key)
+    return conversation_chain, [("System", "PDF processed successfully! Ask me anything about the document.")]
+
+def respond(query, chat_history, conversation_chain):
+    """Handle user queries"""
+    if not conversation_chain:
+        raise gr.Error("Please upload a PDF first")
+    
+    try:
+        result = conversation_chain({"question": query})
+        chat_history.append((query, result["answer"]))
+        return "", chat_history
+    except Exception as e:
+        raise gr.Error(f"Error processing query: {str(e)}")
+
+with gr.Blocks(title="PDF Chatbot", theme=gr.themes.Soft()) as app:
+    gr.Markdown("# 📄 DocuBuddy - Ask Me Questions About Your Document")
+    
+    # State variables
+    conversation_chain = gr.State(None)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            api_key = gr.Textbox(
+                label="OpenAI API Key",
+                type="password",
+                placeholder="Enter your OpenAI API key (sk-...)"
+            )
+            upload_btn = gr.UploadButton(
+                "📁 Upload PDF",
+                file_types=[".pdf"],
+                file_count="single"
+            )
+    
+    chatbot = gr.Chatbot(label="Conversation", height=500)
+    query = gr.Textbox(label="Your Question", placeholder="Type your question here...")
+    clear_btn = gr.ClearButton([query, chatbot])
+    
+    # Event handlers
+    upload_btn.upload(
+        upload_file,
+        [upload_btn, api_key, chatbot],
+        [conversation_chain, chatbot]
+    )
+    
+    query.submit(
+        respond,
+        [query, chatbot, conversation_chain],
+        [query, chatbot]
+    )
+
+if __name__ == "__main__":
+    app.launch(share=True)