'this'

.history/app_20250322171127.yp

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+p

.history/app_20250322171147.py

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+import os
+import streamlit as st
+import requests
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List
+from langchain_groq import ChatGroq
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages.ai import AIMessage
+from fastapi.middleware.cors import CORSMiddleware
+from threading import Thread
+import uvicorn
+
+# 🔹 Set API Keys
+os.environ["GROQ_API_KEY"] = "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"
+os.environ["TAVILY_API_KEY"] = "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"
+
+# 🔹 Initialize FastAPI
+app = FastAPI(title="LangGraph AI Agent")
+
+# 🔹 Allow frontend to communicate with backend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 🔹 Allowed model names
+ALLOWED_MODEL_NAMES = ["llama3-70b-8192", "mixtral-8x7b-32768", "llama-3.3-70b-versatile", "gpt-4o-mini"]
+
+# 🔹 Request Schema
+class RequestState(BaseModel):
+    model_name: str
+    model_provider: str
+    system_prompt: str
+    messages: List[str]
+    allow_search: bool
+
+# 🔹 AI Agent Function
+def get_response_from_ai_agent(llm_id, query, allow_search, system_prompt, provider):
+    if provider == "Groq":
+        llm = ChatGroq(model=llm_id)
+    
+    tools = [TavilySearchResults(max_results=5, api_key=os.environ["TAVILY_API_KEY"])] if allow_search else []
+    
+    agent = create_react_agent(model=llm, tools=tools, state_modifier=system_prompt)
+
+    state = {"message": query}
+    response = agent.invoke(state)
+    
+    messages = response.get("messages")
+    ai_messages = [message.content for message in messages if isinstance(message, AIMessage)]
+    
+    return ai_messages[-1] if ai_messages else "No response from AI."
+
+# 🔹 FastAPI Route
+@app.post("/chat")
+def chat_endpoint(request: RequestState):
+    if request.model_name not in ALLOWED_MODEL_NAMES:
+        return {"error": "Invalid model name"}
+    
+    response = get_response_from_ai_agent(
+        llm_id=request.model_name,
+        query=request.messages,
+        allow_search=request.allow_search,
+        system_prompt=request.system_prompt,
+        provider=request.model_provider
+    )
+    
+    return {"response": response}
+
+# 🔹 Run FastAPI in a separate thread
+def run_fastapi():
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+Thread(target=run_fastapi, daemon=True).start()
+
+# 🔹 Streamlit UI
+st.set_page_config(layout="wide", page_title="LangGraph AI Agent")
+st.title("LangGraph AI Agent 🤖")
+st.markdown("### Chat with an AI Model Powered by LangGraph!")
+
+# 🔹 User Input Fields
+system_prompt = st.text_area("Define your AI Agent:", placeholder="Type your prompt here...")
+provider = st.selectbox("Select the model provider:", ["Groq", "OpenAI"])
+model_name = st.selectbox(
+    "Select the model name:", 
+    ["llama3-70b-8192", "mixtral-8x7b-32768", "llama-3.3-70b-versatile"] if provider == "Groq" else ["gpt-4o-mini"]
+)
+allow_search = st.checkbox("Allow search")
+user_query = st.text_area("Enter query:", placeholder="Type your query here...", height=150)
+
+API_URL = "http://127.0.0.1:8000/chat"
+
+if st.button("Chat 🤖"):
+    if user_query.strip():
+        response = requests.post(API_URL, json={
+            "model_name": model_name,
+            "model_provider": provider,
+            "system_prompt": system_prompt,
+            "messages": [user_query],
+            "allow_search": allow_search
+        })
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if "error" in response_data:
+                st.error(response_data["error"])
+            else:
+                st.success(f"**AI Agent Response:** {response_data['response']}")

Pipfile

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+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+langchain = "*"
+langchain-community = "*"
+langchain-ollama = "*"
+langchain-core = "*"
+faiss-cpu = "*"
+pdfplumber = "*"
+langgraph = "*"
+langchain-groq = "*"
+pydantic = "*"
+fastapi = "*"
+uvicorn = "*"
+streamlit = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.9"

app.py

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+import os
+import streamlit as st
+import requests
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List
+from langchain_groq import ChatGroq
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages.ai import AIMessage
+from fastapi.middleware.cors import CORSMiddleware
+from threading import Thread
+import uvicorn
+
+# 🔹 Set API Keys
+os.environ["GROQ_API_KEY"] = "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"
+os.environ["TAVILY_API_KEY"] = "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"
+
+# 🔹 Initialize FastAPI
+app = FastAPI(title="LangGraph AI Agent")
+
+# 🔹 Allow frontend to communicate with backend
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 🔹 Allowed model names
+ALLOWED_MODEL_NAMES = ["llama3-70b-8192", "mixtral-8x7b-32768", "llama-3.3-70b-versatile", "gpt-4o-mini"]
+
+# 🔹 Request Schema
+class RequestState(BaseModel):
+    model_name: str
+    model_provider: str
+    system_prompt: str
+    messages: List[str]
+    allow_search: bool
+
+# 🔹 AI Agent Function
+def get_response_from_ai_agent(llm_id, query, allow_search, system_prompt, provider):
+    if provider == "Groq":
+        llm = ChatGroq(model=llm_id)
+    
+    tools = [TavilySearchResults(max_results=5, api_key=os.environ["TAVILY_API_KEY"])] if allow_search else []
+    
+    agent = create_react_agent(model=llm, tools=tools, state_modifier=system_prompt)
+
+    state = {"message": query}
+    response = agent.invoke(state)
+    
+    messages = response.get("messages")
+    ai_messages = [message.content for message in messages if isinstance(message, AIMessage)]
+    
+    return ai_messages[-1] if ai_messages else "No response from AI."
+
+# 🔹 FastAPI Route
+@app.post("/chat")
+def chat_endpoint(request: RequestState):
+    if request.model_name not in ALLOWED_MODEL_NAMES:
+        return {"error": "Invalid model name"}
+    
+    response = get_response_from_ai_agent(
+        llm_id=request.model_name,
+        query=request.messages,
+        allow_search=request.allow_search,
+        system_prompt=request.system_prompt,
+        provider=request.model_provider
+    )
+    
+    return {"response": response}
+
+# 🔹 Run FastAPI in a separate thread
+def run_fastapi():
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+Thread(target=run_fastapi, daemon=True).start()
+
+# 🔹 Streamlit UI
+st.set_page_config(layout="wide", page_title="LangGraph AI Agent")
+st.title("LangGraph AI Agent 🤖")
+st.markdown("### Chat with an AI Model Powered by LangGraph!")
+
+# 🔹 User Input Fields
+system_prompt = st.text_area("Define your AI Agent:", placeholder="Type your prompt here...")
+provider = st.selectbox("Select the model provider:", ["Groq", "OpenAI"])
+model_name = st.selectbox(
+    "Select the model name:", 
+    ["llama3-70b-8192", "mixtral-8x7b-32768", "llama-3.3-70b-versatile"] if provider == "Groq" else ["gpt-4o-mini"]
+)
+allow_search = st.checkbox("Allow search")
+user_query = st.text_area("Enter query:", placeholder="Type your query here...", height=150)
+
+API_URL = "http://127.0.0.1:8000/chat"
+
+if st.button("Chat 🤖"):
+    if user_query.strip():
+        response = requests.post(API_URL, json={
+            "model_name": model_name,
+            "model_provider": provider,
+            "system_prompt": system_prompt,
+            "messages": [user_query],
+            "allow_search": allow_search
+        })
+
+        if response.status_code == 200:
+            response_data = response.json()
+            if "error" in response_data:
+                st.error(response_data["error"])
+            else:
+                st.success(f"**AI Agent Response:** {response_data['response']}")