Rename main.py to app.py

app.py

@@ -0,0 +1,42 @@
+from fastapi import FastAPI, HTTPException
+import requests
+import json
+
+app = FastAPI()
+
+# Ollama API endpoint (running inside the container)
+OLLAMA_API = "http://localhost:11434/api/generate"
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the custom LLM API for Qwen3-4B RPG Roleplay"}
+
+@app.post("/generate")
+async def generate(prompt: str):
+    try:
+        # Send request to Ollama API
+        payload = {
+            "model": "hf.co/Chun121/qwen3-4B-rpg-roleplay:Q4_K_M",
+            "prompt": prompt,
+            "stream": False
+        }
+        response = requests.post(OLLAMA_API, json=payload)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException as e:
+        raise HTTPException(status_code=500, detail=f"Error communicating with Ollama: {str(e)}")
+
+# Optional: Add more endpoints as needed
+@app.post("/chat")
+async def chat(prompt: str, context: str = ""):
+    try:
+        payload = {
+            "model": "hf.co/Chun121/qwen3-4B-rpg-roleplay:Q4_K_M",
+            "prompt": f"{context}\nUser: {prompt}",
+            "stream": False
+        }
+        response = requests.post(OLLAMA_API, json=payload)
+        response.raise_for_status()
+        return response.json()
+    except requests.RequestException as e:
+        raise HTTPException(status_code=500, detail=f"Error communicating with Ollama: {str(e)}")

main.py

@@ -1,56 +0,0 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
-from PIL import Image
-import io
-import random
-
-app = FastAPI()
-
-@app.post("/analyze-medical-image/")
-async def analyze_medical_image(file: UploadFile = File(...)):
-    # Validate file type
-    if not file.content_type.startswith("image/"):
-        raise HTTPException(status_code=400, detail="File must be an image")
-
-    try:
-        # Read and validate image
-        contents = await file.read()
-        image = Image.open(io.BytesIO(contents))
-        
-        # Mock medical image analysis
-        mock_results = {
-            "image_size": image.size,
-            "format": image.format,
-            "mock_diagnosis": random.choice([
-                "Normal",
-                "Pneumonia",
-                "Fracture",
-                "Tumor",
-                "Inflammation"
-            ]),
-            "mock_confidence": round(random.uniform(0.65, 0.95), 2),
-            "mock_details": {
-                "region_of_interest": random.choice(["Lung", "Bone", "Brain", "Liver", "None"]),
-                "severity": random.choice(["Mild", "Moderate", "Severe"]),
-                "processing_time_ms": random.randint(200, 600),
-                "recommendation": random.choice([
-                    "Further testing required",
-                    "Consult specialist",
-                    "Monitor condition",
-                    "Immediate treatment advised"
-                ])
-            }
-        }
-        
-        return JSONResponse(content={
-            "status": "success",
-            "results": mock_results,
-            "message": "Medical image processed successfully"
-        })
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")
-
-@app.get("/health")
-async def health_check():
-    return {"status": "healthy"}