Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File
+import pandas as pd
+import faiss
+import pickle
+import numpy as np
+import requests
+import os
+from sentence_transformers import SentenceTransformer
+from exa_py import Exa
+from groq import Groq
+
+app = FastAPI()
+
+# =============================
+# 🔑 KEYS
+# =============================
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+EXA_API_KEY = os.getenv("EXA_API_KEY")
+
+client = Groq(api_key=GROQ_API_KEY)
+exa = Exa(api_key=EXA_API_KEY)
+
+# =============================
+# 🧠 LOAD MODELS
+# =============================
+embed_model = SentenceTransformer('all-mpnet-base-v2')
+
+index = faiss.read_index("faiss_index.index")
+
+with open("startup_texts.pkl", "rb") as f:
+    startup_texts = pickle.load(f)
+
+# =============================
+# 🔍 RETRIEVAL
+# =============================
+def retrieve_similar(problem, k=3):
+    vec = embed_model.encode([problem], convert_to_numpy=True)
+    distances, indices = index.search(vec, k)
+
+    results = []
+    for i, idx in enumerate(indices[0]):
+        results.append({
+            "text": startup_texts[idx],
+            "score": float(distances[0][i])
+        })
+    return results
+
+# =============================
+# 🌐 WEB SEARCH
+# =============================
+def search_web(query):
+    try:
+        response = exa.search(query, num_results=5)
+        return [r.text or r.summary or "" for r in response.results]
+    except:
+        return []
+
+# =============================
+# 🤖 QWEN (Groq)
+# =============================
+def ask_qwen(prompt):
+    completion = client.chat.completions.create(
+        model="qwen/qwen3-32b",
+        messages=[
+            {"role": "system", "content": "You are a strict fact-checking analyst."},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0.3,
+        max_tokens=512
+    )
+    return completion.choices[0].message.content
+
+# =============================
+# 🧠 PIPELINE
+# =============================
+def analyze_problem(problem):
+
+    retrieved = retrieve_similar(problem)
+
+    if retrieved[0]["score"] < 2.0:
+        context = "\n\n".join([r["text"] for r in retrieved])
+    else:
+        web = search_web(problem)
+        context = "\n\n".join(web[:3])
+
+    prompt = f"""
+Problem:
+{problem}
+
+Evidence:
+{context}
+
+Output:
+Status: SOLVED or UNSOLVED
+Reason: one short sentence
+Gaps:
+- bullets
+New Problem:
+rewrite
+"""
+
+    return ask_qwen(prompt)
+
+# =============================
+# 🚀 API
+# =============================
+@app.post("/analyze")
+async def analyze(file: UploadFile = File(...)):
+
+    df = pd.read_csv(file.file)
+
+    results = []
+
+    for p in df.iloc[:, 0].tolist():
+        try:
+            res = analyze_problem(p)
+        except Exception as e:
+            res = f"ERROR: {str(e)}"
+
+        results.append(res)
+
+    return {
+        "success": True,
+        "results": results
+    }