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app.py

@@ -1,191 +1,7 @@
 import gradio as gr
-from roboflow import Roboflow
-import openai
-import cv2
-import os
-import time
-from dotenv import load_dotenv
 
-load_dotenv()
+def greet(name):
+    return "Hello " + name + "!!"
 
-# Retrieve API keys from environment variables
-# ROBOFLOW_API_KEY = os.getenv("ROBOFLOW_API_KEY")
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-# Check if the environment variables are loaded properly
-if not OPENAI_API_KEY:
-    raise ValueError("API keys not found in .env file")
-# Ensure model initialization happens only once here
-rf = Roboflow(api_key="9p4Y2dY8Y6KAT73koAbq")
-project = rf.workspace().project("damaged-vehicle-images")
-roboflow_model = project.version(3).model  # Renaming to avoid any potential overwrite
-
-openai.api_key = OPENAI_API_KEY
-
-def generate_response(prompt):
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo-16k-0613",
-        temperature=0,
-        messages=[
-            {"role": "system", "content": f"{prompt}"}
-        ],
-    )
-    message = response.choices[0].message.content
-    return message
-
-def vehicle_assessment(make, model, year, image):
-    # Save the image to a temporary file
-    temp_filename = "temp_uploaded_image.jpg"
-    cv2.imwrite(temp_filename, image)
-    
-    result = roboflow_model.predict(temp_filename, confidence=40, overlap=30).json()
-
-    pred_l = []
-    for prediction in result['predictions']:
-        pred = prediction['class'].replace("_", " ")
-        pred_l.append(pred)
-
-    # Generate response using OpenAI
-    prompt = f"""I have this data:
-        Make	Model	Year	Type of Damage	Repair Description	Severity	Estimated Labor Hours	Parts Required	Estimated Parts Cost	Labor Rate	Total Estimated Cost
-        Toyota	Camry	2015	Front Bumper	Dent Repair	Minor	2	Bumper, Paint	$200	$50/hr	$300 
-        Honda	Civic	2010	Windshield	Replacement	Major	3	Windshield, Adhesive	$250	$45/hr	$385
-        BMW	X5	2019	Rear Bumper	Scratch Repair	Minor	1.5	Bumper, Paint	$180	$65/hr	$277.5
-        etc 
-
-        and I have this info about a car:
-        {make} , {model} , {year} ,{pred_l}
-        predict the rest of the classes in a similar fashion to the data provided, Repair Description	Estimated Labor Hours	Parts Required	Estimated Parts Cost	Labor Rate	Total Estimated Cost
-
-        DO NOT write sepertae estimaetuon for each damage, make your predictions based on all of them, so for example, if the damages ar like this:
-        severe scratch, severe scratch, medium deformation, severe scratch, severe scratch, severe scratch ... etc,
-      
-        follow this structure:
-
-        Car Damage Assessment and Estimation Report
-
-Vehicle Information:
-
-    Make: [Vehicle Make]
-    Model: [Vehicle Model]
-    Year: [Vehicle Year]
-
-Damage Description:
-
-    Description of Damage: [Describe the damage in detail]
-
-Estimated Repair Costs:
-    
-    Repair Description: Bodywork and Paint Repair ... etc
-    Estimated Labor Hours: [Labor hours Estimate]
-    Parts Required: Paint, Body Filler, Replacement Parts ... etc
-    Estimated Parts Cost: $[Parts Cost Estimate]
-    Labor Rate: $[Labor Cost Estimate]/hr
-    Paint and Materials: $[Paint and Materials Cost Estimate]
-    Total Estimated Repair Cost: $[Total Estimated Cost]
-        
-
-this way you covered all damages and their estimation and you gave me the total thing which I wants
-        
-        """
-    
-    response = generate_response(prompt)
-    
-    # Clean up the temporary file
-    os.remove(temp_filename)
-    return response
-
-with gr.Blocks() as app:
-    # gr.Image(type="pil", value='misbah-green-yellow-logo1.png', width=100, height=100)
-
-
-    gr.Markdown(
-        '<div style="display: inline-block;width: 100%;">'
-        '<img class="img-fluid AnimatedLogo" alt="misbah logo icons" title="misbah logo icons" src="https://img1.wsimg.com/isteam/ip/8a73a484-6b7c-4a18-9dc5-2526353c4068/misbah-green-yellow-logo1.png/:/rs=w:275,h:200,cg:true,m/cr=w:275,h:200/qt=q:95" width="150px">'
-        '</div>'
-    )
-
-    # Define the layout using the Tabs
-    with gr.Tab("Damage Assessment"):
-        gr.Markdown('## Evaluate vehicle damage and estimate repair costs by inputting vehicle details and uploading an image of the damage. Receive a comprehensive repair estimate.')
-        make_input = gr.inputs.Textbox(label="Make")
-        model_input = gr.inputs.Textbox(label="Model")
-        year_input = gr.inputs.Textbox(label="Year")
-        image_input = gr.inputs.Image(label="Upload Image")
-        vehicle_output = gr.outputs.Textbox(label="Estimation Report")
-        vehicle_button = gr.Button("Assess")
-    
-
-    with gr.Tab("Repair Pal Chat"):
-        gr.Markdown('## chat with our Repair Pal about your car problem')
-
-        system_message = {"role": "system", "content": "You are a helpful assistant."}
-        chatbot = gr.Chatbot()
-        msg = gr.Textbox(label='write your problem')
-        clear = gr.Button("Clear")
-
-        state = gr.State([])
-
-        def user(user_message, history):
-            return "", history + [[user_message, None]]
-
-        def bot(history, messages_history):
-            user_message = history[-1][0]
-            bot_message, messages_history = ask_gpt(user_message, messages_history)
-            messages_history += [{"role": "assistant", "content": bot_message}]
-            history[-1][1] = bot_message
-            time.sleep(1)
-            return history, messages_history
-
-        def ask_gpt(message, messages_history):
-            messages_history += [{"role": "user", "content": message}]
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo-16k-0613",
-                messages=messages_history
-            )
-            return response['choices'][0]['message']['content'], messages_history
-
-        def init_history(messages_history):
-            messages_history = []
-            messages_history += [system_message]
-            return messages_history
-
-        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
-            bot, [chatbot, state], [chatbot, state]
-        )
-
-        clear.click(lambda: None, None, chatbot, queue=False).success(init_history, [state], [state])
-
-    def chat_with_bot(chat_input):
-        chat_response = generate_response(chat_input)
-        return chat_response
-
-    
-
- 
-    with gr.Tab("Repair shops and spare parts"):
-        gr.Markdown(""" 
-                    ## Based on the damage assesment report, you can repair your car at these repair shops:
-
-                    [Location](https://maps.app.goo.gl/DwZRi2Kr18fYBQR96?g_st=ic)
-
-                    [Location](https://maps.app.goo.gl/knkiyaLMqYLM86H67?g_st=ic)
-
-                    [Location](https://maps.app.goo.gl/xQSQMytHA3QmshMn6?g_st=ic)
-
-                   ## You can find spare parts for your damaged parts at these websites: 
-                    
-                    [Rafraf](https://rafraf.com/)
-                    
-                    [Afyal](https://afyal.com/) 
-                    
-                    """)
-
-
-
-    # Link the button click to the vehicle assessment function
-    vehicle_button.click(vehicle_assessment, inputs=[make_input, model_input, year_input, image_input], outputs=vehicle_output)
-    # chat_button.click(chat_with_bot, inputs=[chat_input], outputs=chat_output)
-
-    
-app.launch(share=True)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

app2.py

@@ -0,0 +1,191 @@
+import gradio as gr
+from roboflow import Roboflow
+import openai
+import cv2
+import os
+import time
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Retrieve API keys from environment variables
+# ROBOFLOW_API_KEY = os.getenv("ROBOFLOW_API_KEY")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Check if the environment variables are loaded properly
+if not OPENAI_API_KEY:
+    raise ValueError("API keys not found in .env file")
+# Ensure model initialization happens only once here
+rf = Roboflow(api_key="9p4Y2dY8Y6KAT73koAbq")
+project = rf.workspace().project("damaged-vehicle-images")
+roboflow_model = project.version(3).model  # Renaming to avoid any potential overwrite
+
+openai.api_key = OPENAI_API_KEY
+
+def generate_response(prompt):
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo-16k-0613",
+        temperature=0,
+        messages=[
+            {"role": "system", "content": f"{prompt}"}
+        ],
+    )
+    message = response.choices[0].message.content
+    return message
+
+def vehicle_assessment(make, model, year, image):
+    # Save the image to a temporary file
+    temp_filename = "temp_uploaded_image.jpg"
+    cv2.imwrite(temp_filename, image)
+    
+    result = roboflow_model.predict(temp_filename, confidence=40, overlap=30).json()
+
+    pred_l = []
+    for prediction in result['predictions']:
+        pred = prediction['class'].replace("_", " ")
+        pred_l.append(pred)
+
+    # Generate response using OpenAI
+    prompt = f"""I have this data:
+        Make	Model	Year	Type of Damage	Repair Description	Severity	Estimated Labor Hours	Parts Required	Estimated Parts Cost	Labor Rate	Total Estimated Cost
+        Toyota	Camry	2015	Front Bumper	Dent Repair	Minor	2	Bumper, Paint	$200	$50/hr	$300 
+        Honda	Civic	2010	Windshield	Replacement	Major	3	Windshield, Adhesive	$250	$45/hr	$385
+        BMW	X5	2019	Rear Bumper	Scratch Repair	Minor	1.5	Bumper, Paint	$180	$65/hr	$277.5
+        etc 
+
+        and I have this info about a car:
+        {make} , {model} , {year} ,{pred_l}
+        predict the rest of the classes in a similar fashion to the data provided, Repair Description	Estimated Labor Hours	Parts Required	Estimated Parts Cost	Labor Rate	Total Estimated Cost
+
+        DO NOT write sepertae estimaetuon for each damage, make your predictions based on all of them, so for example, if the damages ar like this:
+        severe scratch, severe scratch, medium deformation, severe scratch, severe scratch, severe scratch ... etc,
+      
+        follow this structure:
+
+        Car Damage Assessment and Estimation Report
+
+Vehicle Information:
+
+    Make: [Vehicle Make]
+    Model: [Vehicle Model]
+    Year: [Vehicle Year]
+
+Damage Description:
+
+    Description of Damage: [Describe the damage in detail]
+
+Estimated Repair Costs:
+    
+    Repair Description: Bodywork and Paint Repair ... etc
+    Estimated Labor Hours: [Labor hours Estimate]
+    Parts Required: Paint, Body Filler, Replacement Parts ... etc
+    Estimated Parts Cost: $[Parts Cost Estimate]
+    Labor Rate: $[Labor Cost Estimate]/hr
+    Paint and Materials: $[Paint and Materials Cost Estimate]
+    Total Estimated Repair Cost: $[Total Estimated Cost]
+        
+
+this way you covered all damages and their estimation and you gave me the total thing which I wants
+        
+        """
+    
+    response = generate_response(prompt)
+    
+    # Clean up the temporary file
+    os.remove(temp_filename)
+    return response
+
+with gr.Blocks() as app:
+    # gr.Image(type="pil", value='misbah-green-yellow-logo1.png', width=100, height=100)
+
+
+    gr.Markdown(
+        '<div style="display: inline-block;width: 100%;">'
+        '<img class="img-fluid AnimatedLogo" alt="misbah logo icons" title="misbah logo icons" src="https://img1.wsimg.com/isteam/ip/8a73a484-6b7c-4a18-9dc5-2526353c4068/misbah-green-yellow-logo1.png/:/rs=w:275,h:200,cg:true,m/cr=w:275,h:200/qt=q:95" width="150px">'
+        '</div>'
+    )
+
+    # Define the layout using the Tabs
+    with gr.Tab("Damage Assessment"):
+        gr.Markdown('## Evaluate vehicle damage and estimate repair costs by inputting vehicle details and uploading an image of the damage. Receive a comprehensive repair estimate.')
+        make_input = gr.inputs.Textbox(label="Make")
+        model_input = gr.inputs.Textbox(label="Model")
+        year_input = gr.inputs.Textbox(label="Year")
+        image_input = gr.inputs.Image(label="Upload Image")
+        vehicle_output = gr.outputs.Textbox(label="Estimation Report")
+        vehicle_button = gr.Button("Assess")
+    
+
+    with gr.Tab("Repair Pal Chat"):
+        gr.Markdown('## chat with our Repair Pal about your car problem')
+
+        system_message = {"role": "system", "content": "You are a helpful assistant."}
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(label='write your problem')
+        clear = gr.Button("Clear")
+
+        state = gr.State([])
+
+        def user(user_message, history):
+            return "", history + [[user_message, None]]
+
+        def bot(history, messages_history):
+            user_message = history[-1][0]
+            bot_message, messages_history = ask_gpt(user_message, messages_history)
+            messages_history += [{"role": "assistant", "content": bot_message}]
+            history[-1][1] = bot_message
+            time.sleep(1)
+            return history, messages_history
+
+        def ask_gpt(message, messages_history):
+            messages_history += [{"role": "user", "content": message}]
+            response = openai.ChatCompletion.create(
+                model="gpt-3.5-turbo-16k-0613",
+                messages=messages_history
+            )
+            return response['choices'][0]['message']['content'], messages_history
+
+        def init_history(messages_history):
+            messages_history = []
+            messages_history += [system_message]
+            return messages_history
+
+        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+            bot, [chatbot, state], [chatbot, state]
+        )
+
+        clear.click(lambda: None, None, chatbot, queue=False).success(init_history, [state], [state])
+
+    def chat_with_bot(chat_input):
+        chat_response = generate_response(chat_input)
+        return chat_response
+
+    
+
+ 
+    with gr.Tab("Repair shops and spare parts"):
+        gr.Markdown(""" 
+                    ## Based on the damage assesment report, you can repair your car at these repair shops:
+
+                    [Location](https://maps.app.goo.gl/DwZRi2Kr18fYBQR96?g_st=ic)
+
+                    [Location](https://maps.app.goo.gl/knkiyaLMqYLM86H67?g_st=ic)
+
+                    [Location](https://maps.app.goo.gl/xQSQMytHA3QmshMn6?g_st=ic)
+
+                   ## You can find spare parts for your damaged parts at these websites: 
+                    
+                    [Rafraf](https://rafraf.com/)
+                    
+                    [Afyal](https://afyal.com/) 
+                    
+                    """)
+
+
+
+    # Link the button click to the vehicle assessment function
+    vehicle_button.click(vehicle_assessment, inputs=[make_input, model_input, year_input, image_input], outputs=vehicle_output)
+    # chat_button.click(chat_with_bot, inputs=[chat_input], outputs=chat_output)
+
+    
+app.launch(share=True)