Update app.py

app.py

@@ -1,62 +1,61 @@
-import os
-from flask import Flask, render_template, request, jsonify
-from dotenv import load_dotenv
-
-# USE THE CLASSIC BRIDGE FOR 2026 COMPATIBILITY
-from langchain_classic.agents import AgentExecutor, create_react_agent
-from langchain_classic.tools import Tool
-from langchain_huggingface import HuggingFaceEndpoint
-from langchain.memory import ConversationBufferMemory
-from langchain import hub
-from duckduckgo_search import DDGS
-
-load_dotenv()
-app = Flask(__name__)
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# LLM Setup
-llm = HuggingFaceEndpoint(
-    repo_id="meta-llama/Llama-3.2-3B-Instruct",
-    huggingfacehub_api_token=HF_TOKEN,
-    temperature=0.7,
-    provider="hf-inference"
-)
-
-# Search Tool
-def custom_ddg_search(query: str):
-    try:
-        with DDGS() as ddgs:
-            results = list(ddgs.text(query, max_results=3))
-            return "\n".join([f"{r['title']}: {r['body']}" for r in results])
-    except:
-        return "Search error. Try again."
-
-tools = [
-    Tool(name="web_search", func=custom_ddg_search, description="Search the web.")
-]
-
-# Initialize Agent via Classic methods
-prompt = hub.pull("hwchase17/react")
-agent = create_react_agent(llm, tools, prompt)
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
-agent_executor = AgentExecutor(
-    agent=agent, 
-    tools=tools, 
-    memory=memory, 
-    handle_parsing_errors=True
-)
-
-@app.route("/")
-def index():
-    return render_template("index.html")
-
-@app.route("/ask", methods=["POST"])
-def ask():
-    user_query = request.json.get("query")
-    result = agent_executor.invoke({"input": user_query})
-    return jsonify({"answer": result["output"]})
-
-if __name__ == "__main__":
+import os
+from flask import Flask, render_template, request, jsonify
+from dotenv import load_dotenv
+
+from langchain_classic.agents import AgentExecutor, create_react_agent
+from langchain_classic.tools import Tool
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_classic.memory import ConversationBufferMemory
+from langchain import hub
+from duckduckgo_search import DDGS
+
+load_dotenv()
+app = Flask(__name__)
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# LLM Setup
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Llama-3.2-3B-Instruct",
+    huggingfacehub_api_token=HF_TOKEN,
+    temperature=0.7,
+    provider="hf-inference"
+)
+
+# Search Tool
+def custom_ddg_search(query: str):
+    try:
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=3))
+            return "\n".join([f"{r['title']}: {r['body']}" for r in results])
+    except:
+        return "Search error. Try again."
+
+tools = [
+    Tool(name="web_search", func=custom_ddg_search, description="Search the web.")
+]
+
+# Initialize Agent via Classic methods
+prompt = hub.pull("hwchase17/react")
+agent = create_react_agent(llm, tools, prompt)
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+agent_executor = AgentExecutor(
+    agent=agent, 
+    tools=tools, 
+    memory=memory, 
+    handle_parsing_errors=True
+)
+
+@app.route("/")
+def index():
+    return render_template("index.html")
+
+@app.route("/ask", methods=["POST"])
+def ask():
+    user_query = request.json.get("query")
+    result = agent_executor.invoke({"input": user_query})
+    return jsonify({"answer": result["output"]})
+
+if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860)