Update app.py

app.py

@@ -1,103 +1,48 @@
-# Imports
 import os
-import streamlit as st
 import requests
-from transformers import pipeline
-import openai
-
-# Suppressing all warnings
-import warnings
-warnings.filterwarnings("ignore")
-
-# Image-to-text
-def img2txt(url):
-    print("Initializing captioning model...")
-    captioning_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-    
-    print("Generating text from the image...")
-    text = captioning_model(url, max_new_tokens=20)[0]["generated_text"]
-    
-    print(text)
-    return text
-
-# Text-to-story
-def txt2story(img_text):
-    print("Initializing client...")
-    client = openai.OpenAI(
-        api_key=os.environ["TOGETHER_API_KEY"], 
-        base_url='https://api.together.xyz',
-    )
-    
-    messages = [
-        {"role": "system", "content": '''As an experienced short story writer, write story title and then create a meaningful story influenced by provided words. 
-        Ensure stories conclude positively within 100 words. Remember the story must end within 100 words''', "temperature": 1.8},
-        {"role": "user", "content": f"Here is input set of words: {img_text}", "temperature": 1.5},
-    ]
-    
-    print("Story...")
-    chat_completion = client.chat.completions.create(
-        messages=messages,
-        model="togethercomputer/llama-2-70b-chat")
-    
-    print(chat_completion.choices[0].message.content)
-    return chat_completion.choices[0].message.content
-
-
-
-# Text-to-speech
-def txt2speech(text):
-    print("Initializing text-to-speech conversion...")
-    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
-    headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACEHUB_API_TOKEN']}"}
-    payloads = {'inputs': text}
-
-    response = requests.post(API_URL, headers=headers, json=payloads)
-    
-    with open('audio_story.mp3', 'wb') as file:
-        file.write(response.content)
-
-        
-# Streamlit web app main function
-def main():
-    st.set_page_config(page_title="🎨 Image-to-Audio Story 🎧", page_icon="🖼️")
-    st.title("Turn the Image into Audio Story")
-
-# Allows users to upload an image file
-    uploaded_file = st.file_uploader("# 📷 Upload an image...", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-# Reads and saves uploaded image file
-        bytes_data = uploaded_file.read()
-        with open("uploaded_image.jpg", "wb") as file:
-            file.write(bytes_data)
-
-        st.image(uploaded_file, caption='🖼️ Uploaded Image', use_column_width=True)
-
- # Initiates AI processing and story generation
-        with st.spinner("## 🤖 AI is at Work! "):
-            scenario = img2txt("uploaded_image.jpg")  # Extracts text from the image
-            story = txt2story(scenario)  # Generates a story based on the image text
-            txt2speech(story)  # Converts the story to audio
-
-            st.markdown("---")
-            st.markdown("## 📜 Image Caption")
-            st.write(scenario)
-
-            st.markdown("---")
-            st.markdown("## 📖 Story")
-            st.write(story)
-
-            st.markdown("---")
-            st.markdown("## 🎧 Audio Story")
-            st.audio("audio_story.mp3")
-             
-if __name__ == '__main__':
-    main()
+import streamlit as st
 
-# Credits
-st.markdown("### Credits")
-st.caption('''
-            Made with ❤️ by @Aditya-Neural-Net-Ninja\n 
-            Utilizes Image-to-text, Text Generation, Text-to-speech Transformer Models\n
-            Gratitude to Streamlit, 🤗 Spaces for Deployment & Hosting
-            ''')
+# API details
+API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2-xl"
+API_TOKEN = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
+
+# Streamlit UI
+st.title("GPT-2 Movie Sentiment Analysis")
+
+# Input text for sentiment analysis
+input_text = st.text_area("Enter movie review:", "")
+
+# Choose analysis type
+analysis_type = st.radio("Select analysis type:", ["Zero-shot", "One-shot", "Few-shot"])
+
+if st.button("Analyze Sentiment"):
+    # Prepare payload for API request
+    if analysis_type == "Zero-shot":
+        payload = {"inputs": f"Label the text as either 'positive', 'negative', or 'mixed' related to a movie:\n\n{input_text}"}
+    elif analysis_type == "One-shot":
+        prompt = "Label the sentence as either 'positive', 'negative', or 'mixed' related to a movie:\n\n" \
+                 "Sentence: This movie exceeded my expectations.\nLabel: positive"
+        payload = {"inputs": f"{prompt} {input_text}"}
+    elif analysis_type == "Few-shot":
+        examples = [
+            "Sentence: The cinematography in this movie is outstanding.\nLabel: positive",
+            "Sentence: I didn't enjoy the plot twists in the movie.\nLabel: negative",
+            "Sentence: The acting was great, but the pacing felt off.\nLabel: mixed",
+            "Sentence: This movie didn't live up to the hype.\nLabel: negative",
+        ]
+        prompt = "Label the sentences as either 'positive', 'negative', or 'mixed' related to a movie:\n\n" + "\n".join(examples)
+        payload = {"inputs": f"{prompt}\n\n{input_text}"}
+
+    # Make API request
+    response = requests.post(API_URL, headers=HEADERS, json=payload)
+
+    # Print entire response for debugging
+    st.write("API Response:", response.json())
+
+    # Display results
+    if response.status_code == 200:
+        result = response.json()[0]  # Assuming the sentiment is in the first item of the list
+        st.write("Sentiment:", result.get('generated_text', 'N/A'))
+    else:
+        st.write("Error:", response.status_code, response.text)