final_commit

main.py

@@ -1,5 +1,4 @@
 # Import necessary libraries
-from flask import Flask, render_template, request, jsonify
 from PIL import Image
 from peft import PeftModel
 from PIL import Image
@@ -7,9 +6,12 @@ import torch
 from transformers import AutoProcessor, LlavaForConditionalGeneration, BitsAndBytesConfig
 from deep_translator import GoogleTranslator
 from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
-from fastapi import FastAPI
+from fastapi import FastAPI, Query, UploadFile, File, HTTPException
 from fastapi.staticfiles import StaticFiles
-from fastapi.responses import FileResponse
+from fastapi.responses import FileResponse, JSONResponse
+from io import BytesIO 
+
+
 import warnings 
 # from flask import Flask
 
@@ -20,20 +22,26 @@ app = FastAPI()
     
 warnings.filterwarnings('ignore') 
 
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
+
+@app.get('/echo/')
+async def echo(query_param: str):
+    return {"response": query_param} 
 
 
+# app.mount("/", StaticFiles(directory="static", html=True), name="static")
 
-model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-base_model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16)
 
-# Load the PEFT Lora adapter
-peft_lora_adapter_path = "Praveen0309/llava-1.5-7b-hf-ft-mix-vsft-3"
-peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
-base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
 
-processor = AutoProcessor.from_pretrained("HuggingFaceH4/vsft-llava-1.5-7b-hf-trl")
+# model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
+# quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+# base_model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16)
+
+# # Load the PEFT Lora adapter
+# peft_lora_adapter_path = "Praveen0309/llava-1.5-7b-hf-ft-mix-vsft-3"
+# peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
+# base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
+
+# processor = AutoProcessor.from_pretrained("HuggingFaceH4/vsft-llava-1.5-7b-hf-trl")
 # model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
 # tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
 
@@ -124,43 +132,66 @@ def facebook_response(url, input_sentence):
 
 
 image_cache = {}
-@app.post("/upload/")
-def upload_file():
+@app.post("/upload")
+async def upload_file(file: UploadFile = File(...)):
     try:
-        file = request.files['file']
+        # file = request.files['file']
         if file.filename.endswith('.jpg'):
-            image = Image.open(file.stream)
+            contents = await file.read()
+            image = Image.open(BytesIO(contents))
+            # image = Image.open(file.stream)
             # Store the image in cache (replace with a more suitable storage approach)
             image_cache['image'] = image
             # print("Processing complete. Image stored in cache.")
-            return jsonify({'status': 'success'})
+            return JSONResponse(content={'status': 'success'})
         else:
-            return jsonify({'status': 'error', 'message': 'Uploaded file is not a jpg image.'})
+            # print("dfsd")
+            return JSONResponse(content={'status': 'error', 'message': 'Uploaded file is not a jpg image.'})
     except Exception as e:
-        # print(f"Error during file upload: {e}")
-        return jsonify({'status': 'error', 'message': str(e)})
-
-
-@app.get("/")
-def home():
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
-
-@app.get("/get/")
-def get_bot_response():
+        print(e)
+        return JSONResponse(content={'status': 'error', 'message': str(e)})
+           
+
+
+# @app.get("/get/")
+# async def get_items(msg: str):
+
+#     try:
+#       print( msg ) 
+#       if 'image' in image_cache:
+#           image = image_cache['image']
+#           # print(image)
+#           query = request.args.get('msg')
+#           output = query
+#           # output = google_response(image, query)
+#           return output
+#       else:
+#           return "Please upload an image to continue"
+#     except Exception as e:
+#         return f"Error: {str(e)}"
+
+@app.get("/get")
+async def get_items(msg: str):
     try:
-      if 'image' in image_cache:
-          image = image_cache['image']
-          # print(image)
-          query = request.args.get('msg')
-          output = query
-          # output = google_response(image, query)
-          return output
-      else:
-          return "Please upload an image to continue"
+        # print(msg) 
+        if 'image' in image_cache:
+            image = image_cache['image']
+            # print(image)
+            output = msg  # You can directly use the `msg` parameter here
+            # output = google_response(image, msg)  # Uncomment if you have a function named google_response
+            return output
+        else:
+            # return msg
+            return "Please upload an image to continue"
     except Exception as e:
         return f"Error: {str(e)}"
 
 
+app.mount("/", StaticFiles(directory="static", html=True), name="static")
+@app.get("/")
+def home() -> FileResponse:
+    return FileResponse(path="/app/static/index.html")
+
 # Run the Flask app
 # if __name__ == "__main__":
     app.run(debug = True)

static/script.js

@@ -3,7 +3,7 @@ $(document).ready(function(){
         e.preventDefault();
         $('#uploadStatus').html('<p>Status: Processing...</p>');
         $.ajax({
-            url: "/upload/",
+            url: "/upload",
             type: 'POST',
             data: new FormData(this),
             contentType: false,
@@ -64,7 +64,7 @@ $(document).ready(function(){
             message.draw();
 
             // Call getResponse() to get the chatbot's response
-            $.get("/get/", { msg: text }).done(function(data) {
+            $.get("/get", { msg: text }).done(function(data) {
                 // Draw bot message with bot-message class
                 var botMessage = new Message({
                     text: data,