Update app.py

app.py

@@ -13,41 +13,41 @@ retriever = RagRetriever.from_pretrained(model_name, index_name="exact", use_dum
 model = RagSequenceForGeneration.from_pretrained(model_name, retriever=retriever)
 
 # FAISS Vector Store
-dimension = 768  # Default embedding size for transformers
-index = faiss.IndexFlatL2(dimension)  # L2 distance-based index
-stored_docs = []  # To store document texts alongside vectors
-
-# Function to extract text from uploaded files
-def extract_text(file):
-    if file is None:
-        return "Please upload a document."
-    
-    file_name = file.name
-    file_ext = file_name.split('.')[-1].lower()
-    text = ""
-
-    if file_ext == "txt":
-        text = file.read().decode("utf-8")
-    
-    elif file_ext == "pdf":
-        with pdfplumber.open(file) as pdf:
-            for page in pdf.pages:
-                text += page.extract_text() + "\n"
-
-    elif file_ext == "docx":
-        doc = docx.Document(file)
-        for para in doc.paragraphs:
-            text += para.text + "\n"
-
-    else:
-        return "Unsupported file format! Please upload TXT, PDF, or DOCX."
-
-    # Store document in FAISS index
-    store_in_faiss(text.strip())
-    
-    return text.strip()
-
-# Function to store document in FAISS
+dimension = 768  
+index = faiss.IndexFlatL2(dimension)  
+stored_docs = []  
+chat_history = []  
+
+# Extract text from uploaded files
+def extract_text(files):
+    texts = []
+    for file in files:
+        file_name = file.name
+        file_ext = file_name.split('.')[-1].lower()
+        text = ""
+
+        if file_ext == "txt":
+            text = file.read().decode("utf-8")
+
+        elif file_ext == "pdf":
+            with pdfplumber.open(file) as pdf:
+                for page in pdf.pages:
+                    text += page.extract_text() + "\n"
+
+        elif file_ext == "docx":
+            doc = docx.Document(file)
+            for para in doc.paragraphs:
+                text += para.text + "\n"
+
+        else:
+            return "Unsupported file format! Upload TXT, PDF, or DOCX."
+        
+        texts.append(text.strip())
+        store_in_faiss(text.strip())
+
+    return "\n\n---\n\n".join(texts)  
+
+# Store document in FAISS
 def store_in_faiss(document):
     global index, stored_docs
     if not document.strip():
@@ -58,11 +58,10 @@ def store_in_faiss(document):
     with torch.no_grad():
         embeddings = model.rag.retriever(input_ids=inputs["input_ids"]).cpu().numpy()
     
-    # Add embeddings to FAISS
     index.add(embeddings)
     stored_docs.append(document)
 
-# Function to retrieve top relevant document from FAISS
+# Retrieve the most relevant document from FAISS
 def retrieve_relevant_doc(query):
     if index.ntotal == 0:
         return ""
@@ -72,48 +71,73 @@ def retrieve_relevant_doc(query):
     with torch.no_grad():
         query_embedding = model.rag.retriever(input_ids=inputs["input_ids"]).cpu().numpy()
 
-    # Search in FAISS
     _, top_idx = index.search(query_embedding, k=1)
     return stored_docs[top_idx[0][0]]
 
-# Function to answer questions using RAG with FAISS
-def answer_question(document, question):
-    if not document.strip():
-        return "Please provide document content."
+# Answer questions using RAG with FAISS and maintain chat history
+def chat_with_ai(history, question):
+    if not stored_docs:
+        return history + [[question, "Please upload a document first."]]
 
-    # Retrieve best-matching document
     relevant_doc = retrieve_relevant_doc(question)
-
-    # Use RAG model for answer generation
+    
+    chat_context = "\n".join(["User: " + q + "\nAI: " + a for q, a in history])
+    full_input = f"Context: {chat_context}\n\nDocument: {relevant_doc}\n\nQuestion: {question}"
+    
     inputs = tokenizer(question, relevant_doc, return_tensors="pt", truncation=True)
     with torch.no_grad():
         generated = model.generate(**inputs)
     answer = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
-    
-    return answer
 
-# Gradio UI
-with gr.Blocks() as app:
-    gr.Markdown("# 📄 Advanced RAG NLP Document Editor with FAISS")
+    history.append([question, answer])  
+    return history, answer  
 
-    # File Uploader
-    file_input = gr.File(label="Upload Document (TXT, PDF, DOCX)", type="file")
-    file_output = gr.Textbox(label="Extracted Text (Editable)", lines=12)
+# Gradio UI with Chat Interface, Voice Input & Text-to-Speech
+with gr.Blocks(theme=gr.themes.Soft(), css="""
+    .gradio-container {background-color: #1E1E1E; color: #FFFFFF;}
+    .voice-btn, .speak-btn {background-color: #FFA500; color: black; border-radius: 5px; padding: 5px;}
+""") as app:
+    gr.Markdown("# 🎙️ AI-Powered Document Chatbot with Voice Input & AI Speech", elem_id="title")
 
-    file_input.change(extract_text, inputs=file_input, outputs=file_output)
+    with gr.Row():
+        file_input = gr.File(label="Upload Documents (TXT, PDF, DOCX)", type="file", multiple=True)
+        file_output = gr.Textbox(label="Extracted Text (Editable)", lines=10)
 
-    # Editable Text Editor Canvas
-    editor = gr.Textbox(label="Editor Canvas (Modify Text Before Asking)", lines=12)
+    file_input.change(extract_text, inputs=file_input, outputs=file_output)
 
-    # Update editor with extracted text
+    editor = gr.Textbox(label="Editor Canvas (Modify Extracted Text)", lines=10)
     file_output.change(lambda x: x, inputs=file_output, outputs=editor)
 
-    # Question Answering
-    question_input = gr.Textbox(label="Ask a Question")
-    answer_output = gr.Textbox(label="Answer", lines=2)
-    
-    submit_btn = gr.Button("Get Answer")
-    submit_btn.click(answer_question, inputs=[editor, question_input], outputs=answer_output)
+    chatbot = gr.Chatbot(label="AI Chat Assistant", elem_id="chatbot")
+    question_input = gr.Textbox(label="Ask AI a Question", placeholder="Type or use voice...")
+
+    with gr.Row():
+        send_btn = gr.Button("Send", elem_id="send-btn")
+        voice_btn = gr.Button("🎤 Voice", elem_id="voice-btn")
+        speak_btn = gr.Button("🗣️ Speak Answer", elem_id="speak-btn")
+
+    send_btn.click(chat_with_ai, inputs=[chatbot, question_input], outputs=[chatbot, None])
+
+    voice_btn.click(None, _js="""
+        () => {
+            const recognition = new webkitSpeechRecognition() || new SpeechRecognition();
+            recognition.lang = "en-US";
+            recognition.start();
+            recognition.onresult = function(event) {
+                let transcript = event.results[0][0].transcript;
+                document.querySelector('textarea').value = transcript;
+            };
+        }
+    """)
+
+    speak_btn.click(None, _js="""
+        () => {
+            let lastMsg = document.querySelectorAll('.chat-message:last-child .chat-response')[0].innerText;
+            let utterance = new SpeechSynthesisUtterance(lastMsg);
+            utterance.lang = "en-US";
+            utterance.rate = 1.0;
+            speechSynthesis.speak(utterance);
+        }
+    """)
 
-# Launch in Hugging Face Spaces
 app.launch()