Update app.py

app.py

@@ -1,63 +1,80 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# ✅ UPDATED imports
-from langchain_community.llms import HuggingFacePipeline
+# -----------------------------
+# LlamaIndex (LLM Layer)
+# -----------------------------
+from llama_index.llms.huggingface import HuggingFaceLLM
+
+llm = HuggingFaceLLM(
+    model_name="google/flan-t5-base",
+    tokenizer_name="google/flan-t5-base",
+    context_window=512,
+    max_new_tokens=256,
+    generate_kwargs={
+        "temperature": 0.3,
+        "do_sample": False
+    }
+)
+
+# -----------------------------
+# LangChain (Prompt + Chain)
+# -----------------------------
 from langchain_core.prompts import PromptTemplate
 from langchain.chains import LLMChain
+from langchain.llms.base import LLM
 
-# -------------------------
-# Model (CPU safe)
-# -------------------------
-MODEL_NAME = "google/flan-t5-base"
+# -----------------------------
+# Adapter: LlamaIndex → LangChain
+# -----------------------------
+class LlamaIndexLLMAdapter(LLM):
+    """Adapter to use LlamaIndex LLM inside LangChain"""
 
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+    @property
+    def _llm_type(self):
+        return "llamaindex-huggingface"
 
-hf_pipeline = pipeline(
-    "text2text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_length=256,
-    temperature=0.3
-)
+    def _call(self, prompt: str, stop=None):
+        response = llm.complete(prompt)
+        return response.text
 
-llm = HuggingFacePipeline(pipeline=hf_pipeline)
+# Create LangChain-compatible LLM
+langchain_llm = LlamaIndexLLMAdapter()
 
-# -------------------------
-# Prompt
-# -------------------------
+# Prompt Template
 prompt = PromptTemplate(
     input_variables=["question"],
     template="""
-You are an intelligent AI assistant.
-Answer the question clearly.
+You are a helpful AI assistant.
+Answer clearly and concisely.
 
 Question: {question}
 Answer:
 """
 )
 
-chain = LLMChain(llm=llm, prompt=prompt)
+# LangChain Chain
+chain = LLMChain(
+    llm=langchain_llm,
+    prompt=prompt
+)
 
-# -------------------------
-# Chat function
-# -------------------------
-def chat_fn(user_input):
+# -----------------------------
+# Chat Function
+# -----------------------------
+def chat(user_input):
     if not user_input.strip():
-        return "Please enter a question."
+        return "Please enter a message."
     return chain.run(user_input)
 
-# -------------------------
+# -----------------------------
 # Gradio UI
-# -------------------------
+# -----------------------------
 demo = gr.Interface(
-    fn=chat_fn,
-    inputs=gr.Textbox(lines=2, placeholder="Ask your question..."),
+    fn=chat,
+    inputs=gr.Textbox(lines=2, placeholder="Ask something..."),
     outputs="text",
-    title="LangChain + Hugging Face Chatbot",
-    description="Running on Hugging Face Spaces (LangChain v0.1+ compatible)"
+    title="LangChain + LlamaIndex Chatbot",
+    description="Integrated chatbot (No RAG, No Vector DB)"
 )
 
 demo.launch()
-