Fixed app v2

app.py

@@ -1,43 +1,59 @@
-from fastapi import FastAPI, Form
+import nest_asyncio
+import uvicorn
+from fastapi import FastAPI, File, UploadFile, Form
 from llama_cpp import Llama
-from typing import List
+
 import json
 
+# Allow nested event loops
+nest_asyncio.apply()
+
 # Initialize FastAPI app
 app = FastAPI()
 
 # Load the Llama model
 llm = Llama.from_pretrained(
     repo_id="HuggingFaceTB/SmolLM2-360M-Instruct-GGUF",
-    filename="smollm2-360m-instruct-q8_0.gguf",  # Replace with the actual path to your GGUF file
+    filename="smollm2-360m-instruct-q8_0.gguf"  # Replace with the actual path to your GGUF file
 )
 
-# Endpoint to generate response from model based on user input
-@app.post("/ask/")
-async def ask_question(prompt: str = Form(...)):
-    # Format the prompt as a chat message
-    messages = [
-        {"role": "user", "content": prompt}
-    ]
-    
-    # Generate a response using Llama
-    response = llm.create_chat_completion(messages=messages)
-    response_content = response["choices"][0]["message"]["content"]
+# Endpoint to upload CV file and store CV text
+@app.post("/upload-cv/")
+async def upload_cv(file: UploadFile = File(...)):
+    content = await file.read()
+    cv_text = content.decode("utf-8")
+    return {"cv_text": cv_text}
+
+# Endpoint to compare job descriptions with the CV text
+@app.post("/compare/")
+async def compare_job_cv(job_descriptions: str = Form(...), cv_text: str = Form(...)):
+    # Split job descriptions by line
+    descriptions = job_descriptions.strip().split("\n")
+    results = []
     
-    return {"response": response_content}
-
-# Endpoint to test a simple query (optional)
-@app.get("/test/")
-async def test():
-    # Test the model with a simple question
-    messages = [{"role": "user", "content": "What is the capital of France?"}]
-    response = llm.create_chat_completion(messages=messages)
-    response_content = response["choices"][0]["message"]["content"]
+    for description in descriptions:
+        # Create chat messages to prompt Llama for each job description
+        messages = [
+            {"role": "user", "content": f"Compare the following job description with this resume. Job Description: {description}. Resume: {cv_text}. Give a match score and brief analysis."}
+        ]
+        
+        # Generate response using Llama
+        response = llm.create_chat_completion(messages=messages)
+        response_content = response["choices"][0]["message"]["content"]
+        
+        # Parse response content for a score and summary
+        try:
+            response_data = json.loads(response_content)
+            results.append(response_data)
+        except json.JSONDecodeError:
+            results.append({
+                "Job Description": description,
+                "Analysis": response_content  # Use raw response if JSON parsing fails
+            })
     
-    return {"test_response": response_content}
+    return {"results": results}
 
- 
-import uvicorn
+# Expose the app using ngrok
 
-if __name__ == "__main__":
-    uvicorn.run("main:app", host="0.0.0.0", port=8000)
+# Run the app directly in Colab
+uvicorn.run(app, host="0.0.0.0", port=8000)