Create app.py

app.py

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+# Load Pretrained LLaMA 2 via Hugging Face
+# --------------------------
+llama_model_id = "meta-llama/Llama-2-7b-chat-hf"  # Example: or your custom model ID
+token = "<your_HuggingFace_access_token>"  # Replace with your actual token
+tokenizer = AutoTokenizer.from_pretrained(llama_model_id, token=token)
+model = AutoModelForCausalLM.from_pretrained(llama_model_id, token=token)
+
+# Text generation pipeline
+text_pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=256,
+    temperature=0.7,
+    top_p=0.9
+)
+
+# LangChain LLM wrapper
+llm = HuggingFacePipeline(pipeline=text_pipe)
+
+# --------------------------
+# Load or Create Chroma Vector DB
+# --------------------------
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+persist_dir = "./chroma_db"
+os.makedirs(persist_dir, exist_ok=True)
+vector_db = Chroma(persist_directory=persist_dir, embedding_function=embedding_model)
+retriever = vector_db.as_retriever()
+qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
+
+# --------------------------
+# Whisper for Audio to Text
+# --------------------------
+whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+
+def transcribe_audio(audio):
+    audio = whisper_processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt")
+    result = whisper_model.generate(**audio)
+    return whisper_processor.batch_decode(result, skip_special_tokens=True)[0]
+
+# --------------------------
+# Chatbot and PDF Query Functions
+# --------------------------
+def handle_user_input(text):
+    prompt = f"<s>[INST] {text} [/INST]"
+    response = text_pipe(prompt)[0]["generated_text"].split("[/INST]")[-1].strip()
+    return response
+
+def handle_pdf_query(text):
+    result = qa_chain(text)
+    return result["result"]
+
+# --------------------------
+# Upload & Index New PDF
+# --------------------------
+def upload_and_index_pdf(pdf_file):
+    if pdf_file is None:
+        return "No file uploaded."
+    loader = PyPDFLoader(pdf_file.name)
+    pages = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    docs = text_splitter.split_documents(pages)
+    vector_db.add_documents(docs)
+    vector_db.persist()
+    return f"Uploaded and indexed: {os.path.basename(pdf_file.name)}"
+
+# --------------------------
+# Gradio UI
+# --------------------------
+with gr.Blocks(title="GenAI Customer Support") as demo:
+    gr.Markdown("## 🤖 LLaMA 2 Customer Support Chatbot")
+
+    with gr.Tab("📞 Ask with Voice"):
+        audio_input = gr.Audio(source="microphone", type="numpy")
+        audio_output = gr.Textbox(label="Transcribed Text")
+        audio_button = gr.Button("Transcribe")
+        audio_button.click(fn=transcribe_audio, inputs=audio_input, outputs=audio_output)
+
+    with gr.Tab("💬 Chat with Bot"):
+        user_input = gr.Textbox(placeholder="Ask about booking, trains, etc.", label="Your Query")
+        bot_response = gr.Textbox(label="LLaMA 2 Response")
+        chat_button = gr.Button("Get Response")
+        chat_button.click(fn=handle_user_input, inputs=user_input, outputs=bot_response)
+
+    with gr.Tab("📚 Ask from PDF Knowledge Base"):
+        pdf_input = gr.Textbox(placeholder="Ask about services, policies, etc.", label="Ask PDF")
+        pdf_response = gr.Textbox(label="Knowledge Base Answer")
+        pdf_button = gr.Button("Get Answer")
+        pdf_button.click(fn=handle_pdf_query, inputs=pdf_input, outputs=pdf_response)
+
+    with gr.Tab("📁 Upload PDF to Index"):
+        pdf_file = gr.File(label="Upload PDF")
+        upload_result = gr.Textbox(label="Status")
+        upload_button = gr.Button("Upload & Index")
+        upload_button.click(fn=upload_and_index_pdf, inputs=pdf_file, outputs=upload_result)
+
+# --------------------------
+# Launch
+# --------------------------
+demo.launch()