Create app.py

app.py

@@ -0,0 +1,41 @@
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+app = Flask(__name__)
+
+# Load Mistral 7B model from Hugging Face (ensure your Space is on GPU)
+model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+@app.route("/")
+def home():
+    return "✅ UniSolve (Mistral 7B) backend is running."
+
+@app.route("/generate", methods=["POST"])
+def generate():
+    data = request.get_json()
+    subject = data.get("subject", "")
+    citation = data.get("citation", "")
+    case_study = data.get("case_study", "")
+    questions = data.get("questions", "")
+
+    prompt = (
+        f"Subject: {subject}\n"
+        f"Citation Style: {citation}\n"
+        f"Case Study: {case_study}\n"
+        f"Questions: {questions}\n\n"
+        f"Write a full, detailed academic response. Use {citation} citation style."
+    )
+
+    try:
+        result = generator(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7)
+        return jsonify({"response": result[0]["generated_text"]})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)