Update app.py

app.py

@@ -8,213 +8,109 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
 from langchain.chains import ConversationalRetrievalChain
 from langchain.memory import ConversationBufferMemory
-import torch
-from PIL import Image
-from torchvision import transforms, models
 from langchain_community.llms import HuggingFaceEndpoint
 from langchain_huggingface import HuggingFaceEmbeddings
-from torchvision import transforms, models
 
-class GeometryImageClassifier:
-    def __init__(self):
-        self.model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)  # Updated syntax
-        self.model.fc = torch.nn.Identity()
-        self.model = self.model.to('cpu')
-        self.model.eval()
-        self.transform = transforms.Compose([
-            transforms.Resize((224, 224)),
-            transforms.ToTensor(),
-            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-        ])
-        
-        self.reference_embeddings = {
-            "flat.png": {
-                "embedding": None,
-                "label": "Flat or Sheet-Based"
-            },
-            "cylindrical.png": {
-                "embedding": None,
-                "label": "Cylindrical"
-            },
-            "complex.png": {
-                "embedding": None,
-                "label": "Complex Multi Axis Geometry"
-            }
-        }
-
-    def compute_embedding(self, image_path):
-        img = Image.open(image_path).convert('RGB')
-        img_tensor = self.transform(img).unsqueeze(0)
-        with torch.no_grad():
-            embedding = self.model(img_tensor)
-        return embedding.squeeze().cpu().numpy()
-
-    def initialize_reference_embeddings(self, reference_folder):
-        for image_name in self.reference_embeddings.keys():
-            image_path = os.path.join(reference_folder, image_name)
-            if os.path.exists(image_path):
-                self.reference_embeddings[image_name]["embedding"] = self.compute_embedding(image_path)
-            else:
-                print(f"Warning: Reference image {image_path} not found")
-
-    def find_closest_geometry(self, query_embedding):
-        best_similarity = -1
-        best_label = None
-        
-        for ref_data in self.reference_embeddings.values():
-            if ref_data["embedding"] is not None:
-                similarity = np.dot(query_embedding, ref_data["embedding"])
-                if similarity > best_similarity:
-                    best_similarity = similarity
-                    best_label = ref_data["label"]
-        
-        return best_label or "Unknown Geometry"
-
-    def process_image(self, image_path):
-        query_embedding = self.compute_embedding(image_path)
-        return self.find_closest_geometry(query_embedding)
-
-    
-# ✅ Use a strong sentence embedding model
+# Initialize semantic model
 semantic_model = SentenceTransformer("all-MiniLM-L6-v2")
 
 def extract_text_from_docx(file_path):
-    """ ✅ Extracts normal text & tables from a .docx file for better retrieval. """
     doc = docx.Document(file_path)
     extracted_text = []
-
+    
     for para in doc.paragraphs:
         if para.text.strip():
             extracted_text.append(para.text.strip())
-
+    
     for table in doc.tables:
         extracted_text.append("📌 Table Detected:")
         for row in table.rows:
             row_text = [cell.text.strip() for cell in row.cells]
             if any(row_text):
                 extracted_text.append(" | ".join(row_text))
-
+    
     return "\n".join(extracted_text)
 
-
 def load_documents():
-    """ ✅ Loads & processes documents, ensuring table data is properly extracted. """
     file_paths = {
         "Fastener_Types_Manual": "Fastener_Types_Manual.docx",
         "Manufacturing_Expert_Manual": "Manufacturing Expert Manual.docx"
     }
-
+    
     all_splits = []
-
+    
     for doc_name, file_path in file_paths.items():
         if not os.path.exists(file_path):
             raise FileNotFoundError(f"Document not found: {file_path}")
-
+        
         print(f"Extracting text from {file_path}...")
         full_text = extract_text_from_docx(file_path)
-
+        
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=200)
         doc_splits = text_splitter.create_documents([full_text])
-
+        
         for chunk in doc_splits:
             chunk.metadata = {"source": doc_name}
-
+        
         all_splits.extend(doc_splits)
-
+    
     return all_splits
 
-
 def create_db(splits):
-    """ ✅ Creates a FAISS vector database from document splits. """
     embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
     vectordb = FAISS.from_documents(splits, embeddings)
-    return vectordb
-
+    return vectordb, embeddings
 
 def retrieve_documents(query, retriever, embeddings):
-    """ ✅ Retrieves the most relevant documents & filters out low-relevance ones. """
     query_embedding = np.array(embeddings.embed_query(query)).reshape(1, -1)
     results = retriever.invoke(query)
-
+    
     if not results:
         return []
-
+    
     doc_embeddings = np.array([embeddings.embed_query(doc.page_content) for doc in results])
-    similarity_scores = cosine_similarity(query_embedding, doc_embeddings)[0]  # ✅ Proper cosine similarity
-
-    MIN_SIMILARITY = 0.5  # 🔥 Increased threshold to improve relevance
+    similarity_scores = cosine_similarity(query_embedding, doc_embeddings)[0]
+    
+    MIN_SIMILARITY = 0.5
     filtered_results = [(doc, sim) for doc, sim in zip(results, similarity_scores) if sim >= MIN_SIMILARITY]
-
-    # ✅ Debugging log
+    
     print(f"🔍 Query: {query}")
-    print(f"📄 Retrieved Docs (before filtering): {[(doc.metadata.get('source', 'Unknown'), sim) for doc, sim in zip(results, similarity_scores)]}")
-    print(f"✅ Filtered Docs (after threshold {MIN_SIMILARITY}): {[(doc.metadata.get('source', 'Unknown'), sim) for doc, sim in filtered_results]}")
-
+    print(f"📄 Retrieved Docs: {[(doc.metadata.get('source', 'Unknown'), sim) for doc, sim in filtered_results]}")
+    
     return [doc for doc, _ in filtered_results] if filtered_results else []
 
-
 def validate_query_semantically(query, retrieved_docs):
-    """ ✅ Ensures the query meaning is covered in the retrieved documents. """
     if not retrieved_docs:
         return False
-
+    
     combined_text = " ".join([doc.page_content for doc in retrieved_docs])
     query_embedding = semantic_model.encode(query, normalize_embeddings=True)
     doc_embedding = semantic_model.encode(combined_text, normalize_embeddings=True)
-
-    similarity_score = np.dot(query_embedding, doc_embedding)  # ✅ Cosine similarity already normalized
-
+    
+    similarity_score = np.dot(query_embedding, doc_embedding)
     print(f"🔍 Semantic Similarity Score: {similarity_score}")
+    
+    return similarity_score >= 0.3
 
-    return similarity_score >= 0.3  # 🔥 Stricter threshold to ensure correctness
-
-
-def handle_query(query, history, retriever, qa_chain, embeddings):
-    """ ✅ Handles user queries & prevents hallucination. """
-    retrieved_docs = retrieve_documents(query, retriever, embeddings)
-
-    if not retrieved_docs or not validate_query_semantically(query, retrieved_docs):
-        return history + [(query, "I couldn't find any relevant information.")], ""
-
-    response = qa_chain.invoke({"question": query, "chat_history": history})
-    assistant_response = response['answer'].strip()
-
-    # ✅ Final hallucination check
-    if not validate_query_semantically(query, retrieved_docs):
-        assistant_response = "I couldn't find any relevant information."
-
-    assistant_response += f"\n\n📄 **Source:** {', '.join(set(doc.metadata.get('source', 'Unknown') for doc in retrieved_docs))}"
-
-    # ✅ Debugging logs
-    print(f"🤖 LLM Response: {assistant_response[:300]}")  # ✅ Limit output for debugging
-
-    history.append((query, assistant_response))
-    return history, ""
-
-
-def initialize_chatbot(vector_db):
-    """ ✅ Initializes chatbot with improved retrieval & processing. """
+def initialize_chatbot(vector_db, embeddings):
     memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key='answer')
-
-    embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
-
-    retriever = vector_db.as_retriever(search_kwargs={"k": 5, "search_type": "similarity"})
-
-    system_prompt = """You are an AI assistant that answers questions **ONLY based on the provided documents**.
-- **If no relevant documents are retrieved, respond with: "I couldn't find any relevant information."**
-- **If the meaning of the query does not match the retrieved documents, say "I couldn't find any relevant information."**
-- **Do NOT attempt to answer from general knowledge.**
-"""
-
+    
+    retriever = vector_db.as_retriever(search_kwargs={"k": 5})
+    
+    system_prompt = """You are an AI assistant that answers questions ONLY based on the provided documents.
+    - If no relevant documents are retrieved, respond with: "I couldn't find any relevant information."
+    - If the meaning of the query does not match the retrieved documents, say "I couldn't find any relevant information."
+    - Do NOT attempt to answer from general knowledge."""
+    
     llm = HuggingFaceEndpoint(
-        repo_id="mistralai/Mistral-7B-Instruct-v0.2",
+        repo_id="openai-community/gpt2",
         huggingfacehub_api_token=os.environ.get("HUGGINGFACE_API_TOKEN"),
         temperature=0.1,
-        max_new_tokens=400,  
+        max_new_tokens=400,
         task="text-generation",
         system_prompt=system_prompt
     )
-
+    
     qa_chain = ConversationalRetrievalChain.from_llm(
         llm=llm,
         retriever=retriever,
@@ -222,54 +118,40 @@ def initialize_chatbot(vector_db):
         return_source_documents=True,
         verbose=False
     )
+    
+    return retriever, qa_chain
 
-    return retriever, qa_chain, embeddings
-
-
-def process_image_and_generate_query(image):
-    classifier = GeometryImageClassifier()
-    geometry_type = classifier.process_image(image)
+def handle_query(query, history, retriever, qa_chain, embeddings):
+    retrieved_docs = retrieve_documents(query, retriever, embeddings)
+    
+    if not retrieved_docs or not validate_query_semantically(query, retrieved_docs):
+        return history + [(query, "I couldn't find any relevant information.")], ""
+    
+    response = qa_chain.invoke({"question": query, "chat_history": history})
+    assistant_response = response['answer'].strip()
+    
+    if not validate_query_semantically(query, retrieved_docs):
+        assistant_response = "I couldn't find any relevant information."
     
-    query = f"I have a {geometry_type} geometry, which screw should I use and what is the best machine to use for {geometry_type} geometry?"
-    return geometry_type, query
+    assistant_response += f"\n\n📄 Source: {', '.join(set(doc.metadata.get('source', 'Unknown') for doc in retrieved_docs))}"
+    
+    history.append((query, assistant_response))
+    return history, ""
 
 def demo():
-    # Initialize classifier once at startup
-    classifier = GeometryImageClassifier()
-    classifier.initialize_reference_embeddings("images")
-    
-    # Initialize chatbot components
-    retriever, qa_chain, embeddings = initialize_chatbot(create_db(load_documents()))
+    documents = load_documents()
+    vector_db, embeddings = create_db(documents)
+    retriever, qa_chain = initialize_chatbot(vector_db, embeddings)
     
     with gr.Blocks() as app:
-        gr.Markdown("### 🤖 **Fastener Agent with Image Recognition** 📚")
+        gr.Markdown("### 🤖 Document Question Answering System")
+        
+        chatbot = gr.Chatbot()
+        query_input = gr.Textbox(label="Ask a question about the documents")
+        query_btn = gr.Button("Submit")
         
-        with gr.Row():
-            with gr.Column(scale=1):
-                image_input = gr.Image(type="filepath", label="Upload Geometry Image")
-                geometry_label = gr.Textbox(label="Detected Geometry Type", interactive=False)
-                
-            with gr.Column(scale=2):
-                chatbot = gr.Chatbot()
-                query_input = gr.Textbox(label="Ask me a question")
-                query_btn = gr.Button("Ask")
-
-        def image_upload_handler(image):
-            if image is None:
-                return "", ""
-            # Use the initialized classifier
-            geometry_type = classifier.process_image(image)
-            suggested_query = f"I have a {geometry_type} geometry, which screw should I use and what is the best machine to use for {geometry_type} geometry?"
-            return geometry_type, suggested_query
-
         def user_query_handler(query, history):
             return handle_query(query, history, retriever, qa_chain, embeddings)
-
-        image_input.change(
-            image_upload_handler,
-            inputs=[image_input],
-            outputs=[geometry_label, query_input]
-        )
         
         query_btn.click(
             user_query_handler,
@@ -282,8 +164,8 @@ def demo():
             inputs=[query_input, chatbot],
             outputs=[chatbot, query_input]
         )
-
+    
     app.launch()
 
 if __name__ == "__main__":
-    demo()
+    demo()