Update app.py

app.py

@@ -1,58 +1,53 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
+import gradio as gr
 import requests
 import os
 import faiss
 import numpy as np
 import json
 from sentence_transformers import SentenceTransformer
+from datetime import datetime
 
-# ✅ Load your texts and vector index
+# ✅ Load context and model
 with open("texts.json", "r", encoding="utf-8") as f:
     texts = json.load(f)
 
 index = faiss.read_index("faiss_index.bin")
 embed_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# ✅ Setup your API key and model name
-API_KEY = os.environ.get("OPENROUTER_API_KEY")  # Set this in environment or .env file
+API_KEY = os.environ.get("OPENROUTER_API_KEY")
 MODEL = "qwen/qwen-2.5-coder-32b-instruct:free"
 
-# ✅ Define FastAPI app
-app = FastAPI()
-
-# ✅ Request schema
-class ChatRequest(BaseModel):
-    message: str
-    history: list = []  # Optional, not used in current logic
-
-# ✅ Function to get top-k similar chunks from FAISS
-def get_context(query, top_k=5):
+# ✅ Get context
+def get_context(query, top_k=5, threshold=0.45):
     query_vec = embed_model.encode([query])
     D, I = index.search(np.array(query_vec), top_k)
+    if all(score < threshold for score in D[0]):
+        return None
     return "\n".join([texts[i] for i in I[0]])
 
-# ✅ Endpoint
-@app.post("/chat")
-def chat_api(data: ChatRequest):
-    user_message = data.message
-    context = get_context(user_message)
+# ✅ Chat logic
+def chat_fn(message, history):
+    context = get_context(message)
+    if context is None:
+        return history + [(message, "❌ Sorry! I cannot answer that.")], gr.update(visible=True)
+
+    headers = {
+        "Authorization": f"Bearer {API_KEY}",
+        "Content-Type": "application/json"
+    }
 
     messages = [
         {
             "role": "system",
-            "content": f"You are a helpful coding assistant. Use the following context to answer the question:\n{context}"
-        },
-        {
-            "role": "user",
-            "content": user_message
+            "content": f"You are a helpful assistant. Only answer using this context:\n{context}\nIf the answer is not in the context, reply with 'Sorry! I cannot answer that.'"
         }
     ]
 
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json"
-    }
+    for user, assistant in history:
+        messages.append({"role": "user", "content": user})
+        messages.append({"role": "assistant", "content": assistant})
+
+    messages.append({"role": "user", "content": message})
 
     payload = {
         "model": MODEL,
@@ -62,9 +57,44 @@ def chat_api(data: ChatRequest):
     try:
         response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
         response.raise_for_status()
-        result = response.json()
-        reply = result["choices"][0]["message"]["content"]
+        reply = response.json()["choices"][0]["message"]["content"]
     except Exception as e:
-        reply = f"❌ Error: {str(e)}"
+        reply = f"❌ Error: {e}"
+
+    return history + [(message, reply)], gr.update(visible=True)
+
+# ✅ Export chat
+def export_logs(history):
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+    filename = f"chat_log_{timestamp}.txt"
+    log_text = "\n\n".join([f"You: {q}\nBot: {a}" for q, a in history])
+    with open(filename, "w", encoding="utf-8") as f:
+        f.write(log_text)
+    return filename
+
+# ✅ Gradio UI with typing animation + auto-scroll + export button
+with gr.Blocks(css=".footer {display: none !important;}") as demo:
+    chatbot = gr.Chatbot()
+    state = gr.State([])
+    with gr.Row():
+        msg = gr.Textbox(placeholder="Type your message and press enter...", scale=8)
+        export_btn = gr.Button("Export Chat", scale=1, visible=False)
+
+    typing_indicator = gr.Textbox(value="", visible=False, interactive=False, show_label=False)
+
+    def user_send(message, history):
+        chatbot.update(value=history + [(message, "⏳ ...")])  # Typing animation
+        return "", history + [(message, "⏳ ...")], gr.update(visible=False)
+
+    def complete_chat(message, history):
+        return chat_fn(message, history)
+
+    msg.submit(user_send, [msg, state], [msg, chatbot, export_btn]).then(
+        complete_chat, [msg, state], [chatbot, export_btn]
+    )
+
+    export_btn.click(fn=export_logs, inputs=[state], outputs=gr.File(label="Download Chat Log"))
+
+    chatbot.style(height=500)
 
-    return {"reply": reply}
+demo.launch()