Fixed app v2

app.py

@@ -1,10 +1,6 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
+import gradio as gr
 from llama_cpp import Llama
 
-# Initialize FastAPI app
-app = FastAPI()
-
 # Load the SecurityLLM model
 try:
     llm = Llama.from_pretrained(
@@ -14,50 +10,48 @@ try:
 except Exception as e:
     raise RuntimeError(f"Failed to load model: {e}")
 
-# Define request model for CV matching
-class CVMatchRequest(BaseModel):
-    cv_text: str
-    job_descriptions: str
-
-# Define response model
-class CVMatchResponse(BaseModel):
-    results: list
-
-# Define the route for CV and job description matching
-@app.post("/match_cv_to_jobs", response_model=CVMatchResponse)
-async def match_cv_to_jobs(request: CVMatchRequest):
-    try:
-        # Split job descriptions by line
-        descriptions = request.job_descriptions.strip().split("\n")
-        results = []
+# Function to match CV to job descriptions
+def match_cv_to_jobs(cv_text, job_descriptions):
+    # Split job descriptions by line
+    descriptions = job_descriptions.strip().split("\n")
+    results = []
+    
+    for description in descriptions:
+        # Create a prompt to compare the CV with each job description
+        prompt = (
+            f"Compare the following job description with this resume. Job Description: {description}. "
+            f"Resume: {cv_text}. Provide a match score and a brief analysis."
+        )
         
-        for description in descriptions:
-            # Create a prompt to compare the CV with each job description
-            prompt = (
-                f"Compare the following job description with this resume. Job Description: {description}. "
-                f"Resume: {request.cv_text}. Provide a match score and a brief analysis."
-            )
-            
-            # Generate response from the model
-            response = llm.create_chat_completion(
-                messages=[
-                    {
-                        "role": "user",
-                        "content": prompt
-                    }
-                ]
-            )
-            
-            # Extract and return the analysis text
-            analysis_text = response["choices"][0]["message"]["content"]
-            results.append({
-                "Job Description": description,
-                "Analysis": analysis_text
-            })
+        # Generate response from the model
+        response = llm.create_chat_completion(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ]
+        )
         
-        return CVMatchResponse(results=results)
+        # Extract and store the analysis text
+        analysis_text = response["choices"][0]["message"]["content"]
+        results.append({
+            "Job Description": description,
+            "Analysis": analysis_text
+        })
+    
+    return results
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# CV to Job Description Matcher")
+    
+    cv_text = gr.Textbox(label="CV Text", placeholder="Enter the CV text here", lines=10)
+    job_descriptions = gr.Textbox(label="Job Descriptions (one per line)", placeholder="Enter each job description on a new line", lines=5)
+    match_button = gr.Button("Match CV to Job Descriptions")
+    
+    output = gr.JSON(label="Match Results")
     
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+    match_button.click(fn=match_cv_to_jobs, inputs=[cv_text, job_descriptions], outputs=output)
 
-# To run the app, use: uvicorn app:app --reload
+demo.launch()