Update app.py

app.py

@@ -1,46 +1,19 @@
 import os
-import copy
 import base64
 import requests
-import tempfile
 import secrets
 import gradio as gr
 from huggingface_hub import upload_file, InferenceClient
 from PIL import Image
+import json
 
-# --- Config ---
-HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN")
-HF_DATASET_REPO = "OppaAI/Robot_MCP"
-# Model specifically for VLM (image-to-text) tasks on Hugging Face
-HF_VLM_MODEL = "Qwen/Qwen2.5-VL-7B-Instruct" # A suitable VLM model
-
-if not HF_TOKEN:
-    raise ValueError("HF_TOKEN environment variable not set.")
+# ... (Config and Helper Functions remain the same as previous snippets) ...
+# Ensure HF_VLM_MODEL is set back to the Qwen model you want to use:
+HF_VLM_MODEL = "Qwen/Qwen2.5-VL-7B-Instruct" 
 
 # Initialize the Hugging Face Inference Client
 hf_client = InferenceClient(token=HF_TOKEN)
 
-# --- Helper Functions ---
-def save_and_upload_image(image_b64):
-    """Save image to /tmp and upload to HF dataset."""
-    image_bytes = base64.b64decode(image_b64)
-    # Use a unique filename to prevent conflicts in /tmp
-    local_tmp_path = f"/tmp/uploaded_image_{secrets.token_hex(8)}.jpg" 
-    with open(local_tmp_path, "wb") as f:
-        f.write(image_bytes)
-
-    path_in_repo = f"images/uploaded_image_{secrets.token_hex(8)}.jpg"
-    upload_file(
-        path_or_fileobj=local_tmp_path,
-        path_in_repo=path_in_repo,
-        repo_id=HF_DATASET_REPO,
-        token=HF_TOKEN,
-        repo_type="dataset"
-    )
-
-    hf_image_url = f"https://huggingface.co/datasets/{HF_DATASET_REPO}/resolve/main/{path_in_repo}"
-    return local_tmp_path, hf_image_url, path_in_repo, len(image_bytes)
-
 # --- Main MCP function ---
 def process_and_describe(payload: dict):
     try:
@@ -49,20 +22,35 @@ def process_and_describe(payload: dict):
 
         # 1️⃣ Save & upload image
         local_tmp_path, hf_url, path_in_repo, size_bytes = save_and_upload_image(image_b64)
-
-        # 2️⃣ Prepare prompt (optional, some models ignore this for basic image_to_text)
-        # prompt = "Describe this image in detail." 
         
-        # Open the image using PIL for the InferenceClient
-        image = Image.open(local_tmp_path)
+        # --- NEW VLM CALL LOGIC ---
+        # 2️⃣ Prepare prompt in the Qwen specific format (using Markdown for image embedding)
+        # The API expects an image embedded in the prompt using Markdown syntax.
+        prompt_text = "Describe this image in detail."
+        
+        # Base64 encode the image for embedding in the JSON payload
+        with open(local_tmp_path, "rb") as f:
+            image_b64_encoded_string = base64.b64encode(f.read()).decode("utf-8")
+
+        # The full prompt format required by Qwen in the API
+        full_prompt = f'<img src="data:image/jpeg;base64,{image_b64_encoded_string}">{prompt_text}'
 
-        # 3️⃣ Call VLM using Hugging Face Inference Client
-        # Removed the problematic 'details' argument
-        vlm_text = hf_client.image_to_text(
-            image=image,
+        # 3️⃣ Call VLM using hf_client.post (low-level API call for specific models)
+        # We use the 'text-generation' task endpoint as indicated by the error message.
+        api_response = hf_client.post(
+            json={"inputs": full_prompt, "parameters": {"max_new_tokens": 150}},
             model=HF_VLM_MODEL,
-            # details=True # <-- REMOVED THIS LINE
+            task="text-generation"
         )
+        
+        # The response is usually a list of dicts, extract the generated text
+        # Example response format: [{'generated_text': '... description ...'}]
+        if isinstance(api_response, list) and len(api_response) > 0:
+            vlm_text = api_response[0].get('generated_text', '').strip()
+        else:
+            vlm_text = "Failed to parse VLM response."
+
+        # --- END NEW VLM CALL LOGIC ---
 
         return {
             "saved_to_hf_hub": True,
@@ -75,17 +63,10 @@ def process_and_describe(payload: dict):
         }
 
     except Exception as e:
+        # Added better error handling as suggested previously
         return {"error": str(e)}
 
-# --- Gradio MCP Interface ---
-demo = gr.Interface(
-    fn=process_and_describe,
-    inputs=gr.JSON(label="Input Payload (Dict format with 'image_b64')"),
-    outputs=gr.JSON(label="Reply to Jetson"),
-    api_name="predict"
-)
+# ... (Gradio Interface code remains the same) ...
 
 if __name__ == "__main__":
-    # You will need to install the required libraries:
-    # pip install gradio huggingface_hub Pillow requests
     demo.launch(mcp_server=True)